Spatio-temporal modeling of nonlinear distributed parameter systems
Li, Han-Xiong
2011-01-01
The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s
Numerical studies of identification in nonlinear distributed parameter systems
Banks, H. T.; Lo, C. K.; Reich, Simeon; Rosen, I. G.
1989-01-01
An abstract approximation framework and convergence theory for the identification of first and second order nonlinear distributed parameter systems developed previously by the authors and reported on in detail elsewhere are summarized and discussed. The theory is based upon results for systems whose dynamics can be described by monotone operators in Hilbert space and an abstract approximation theorem for the resulting nonlinear evolution system. The application of the theory together with numerical evidence demonstrating the feasibility of the general approach are discussed in the context of the identification of a first order quasi-linear parabolic model for one dimensional heat conduction/mass transport and the identification of a nonlinear dissipation mechanism (i.e., damping) in a second order one dimensional wave equation. Computational and implementational considerations, in particular, with regard to supercomputing, are addressed.
Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.
Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong
2015-11-01
The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.
An approximation theory for the identification of nonlinear distributed parameter systems
Banks, H. T.; Reich, Simeon; Rosen, I. G.
1990-01-01
An abstract approximation framework for the identification of nonlinear distributed parameter systems is developed. Inverse problems for nonlinear systems governed by strongly maximal monotone operators (satisfying a mild continuous dependence condition with respect to the unknown parameters to be identified) are treated. Convergence of Galerkin approximations and the corresponding solutions of finite dimensional approximating identification problems to a solution of the original finite dimensional identification problem is demonstrated using the theory of nonlinear evolution systems and a nonlinear analog of the Trotter-Kato appproximation result for semigroups of bounded linear operators. The nonlinear theory developed here is shown to subsume an existing linear theory as a special case. It is also shown to be applicable to a broad class of nonlinear elliptic operators and the corresponding nonlinear parabolic partial differential equations to which they lead. An application of the theory to a quasilinear model for heat conduction or mass transfer is discussed.
Estimation on nonlinear damping in second order distributed parameter systems
Banks, H. T.; Reich, Simeon; Rosen, I. G.
1989-01-01
An approximation and convergence theory for the identification of nonlinear damping in abstract wave equations is developed. It is assumed that the unknown dissipation mechanism to be identified can be described by a maximal monotone operator acting on the generalized velocity. The stiffness is assumed to be linear and symmetric. Functional analytic techniques are used to establish that solutions to a sequence of finite dimensional (Galerkin) approximating identification problems in some sense approximate a solution to the original infinite dimensional inverse problem.
Parameter estimation in nonlinear distributed systems - Approximation theory and convergence results
Banks, H. T.; Reich, Simeon; Rosen, I. G.
1988-01-01
An abstract approximation framework and convergence theory is described for Galerkin approximations applied to inverse problems involving nonlinear distributed parameter systems. Parameter estimation problems are considered and formulated as the minimization of a least-squares-like performance index over a compact admissible parameter set subject to state constraints given by an inhomogeneous nonlinear distributed system. The theory applies to systems whose dynamics can be described by either time-independent or nonstationary strongly maximal monotonic operators defined on a reflexive Banach space which is densely and continuously embedded in a Hilbert space. It is demonstrated that if readily verifiable conditions on the system's dependence on the unknown parameters are satisfied, and the usual Galerkin approximation assumption holds, then solutions to the approximating problems exist and approximate a solution to the original infinite-dimensional identification problem.
THE STUDY ON A KIND OF CONTROL SYSTEM WITH NONLINEAR PARABOLIC DISTRIBUTED PARAMETERS
Institute of Scientific and Technical Information of China (English)
周建军; 徐燕侯
2002-01-01
The modelling of one kind of nonlinear parabolic distributed parameter control system with moving boundary, which had extensive applications was presented. Two methods were used to investigate the basic characteristics of the system: 1 ) transforming the system in the variable domain into that in the fixed domain; 2) transforming the distributed parameter system into the lumped parameter system. It is found that there are two critical values for the control variable: the larger one determines whether or not the boundary would move, while the smaller one determines whether or not the boundary would stop automatically. For one-dimensional system of planar, cylindrical and spherical cases the definite solution problem can be expressed as a unified form. By means of the computer simulation the open-loop control system and close-cycle feedback control system have been investigated. Numerical results agree well with theoretical results. The computer simulation shows that the system is well posed, stable, measurable and controllable.
Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing
2016-12-08
Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.
Rodríguez, Hugo; Schaft, Arjan J. van der; Ortega, Romeo
2001-01-01
Energy-shaping techniques have been successfully used for stabilization of nonlinear finite dimensional systems for 20 years now. In particular, for systems described by Port-Controlled Hamiltonian (PCH) models, the “control by interconnection” method provides a simple and elegant procedure for stab
Rodríguez, Hugo; Schaft, van der Arjan J.; Ortega, Romeo
2001-01-01
Energy-shaping techniques have been successfully used for stabilization of nonlinear finite dimensional systems for 20 years now. In particular, for systems described by Port-Controlled Hamiltonian (PCH) models, the "control by interconnection" method provides a simple and elegant procedure for stab
Directory of Open Access Journals (Sweden)
Cheng-Dong Yang
2014-01-01
Full Text Available This paper addresses the problem of robust H∞ control design via the proportional-spatial derivative (P-sD control approach for a class of nonlinear distributed parameter systems modeled by semilinear parabolic partial differential equations (PDEs. By using the Lyapunov direct method and the technique of integration by parts, a simple linear matrix inequality (LMI based design method of the robust H∞ P-sD controller is developed such that the closed-loop PDE system is exponentially stable with a given decay rate and a prescribed H∞ performance of disturbance attenuation. Moreover, a suboptimal H∞ controller is proposed to minimize the attenuation level for a given decay rate. The proposed method is successfully employed to address the control problem of the FitzHugh-Nagumo (FHN equation, and the achieved simulation results show its effectiveness.
Distributed nonlinear optical response
DEFF Research Database (Denmark)
Nikolov, Nikola Ivanov
2005-01-01
The purpose of the research presented here is to investigate basic physical properties in nonlinear optical materials with delayed or nonlocal nonlinearity. Soliton propagation, spectral broadening and the influence of the nonlocality or delay of the nonlinearity are the main focusses in the work...
Hallbauer-Zadorozhnaya, Valeriya; Santarato, Giovanni; Abu Zeid, Nasser
2015-08-01
In this paper, two separate but related goals are tackled. The first one is to demonstrate that in some saturated rock textures the non-linear behaviour of induced polarization (IP) and the violation of Ohm's law not only are real phenomena, but they can also be satisfactorily predicted by a suitable physical-mathematical model, which is our second goal. This model is based on Fick's second law. As the model links the specific dependence of resistivity and chargeability of a laboratory sample to the injected current and this in turn to its pore size distribution, it is able to predict pore size distribution from laboratory measurements, in good agreement with mercury injection capillary pressure test results. This fact opens up the possibility for hydrogeophysical applications on a macro scale. Mathematical modelling shows that the chargeability acquired in the field under normal conditions, that is at low current, will always be very small and approximately proportional to the applied current. A suitable field test site for demonstrating the possible reliance of both resistivity and chargeability on current was selected and a specific measuring strategy was established. Two data sets were acquired using different injected current strengths, while keeping the charging time constant. Observed variations of resistivity and chargeability are in agreement with those predicted by the mathematical model. These field test data should however be considered preliminary. If confirmed by further evidence, these facts may lead to changing the procedure of acquiring field measurements in future, and perhaps may encourage the design and building of a new specific geo-resistivity meter. This paper also shows that the well-known Marshall and Madden's equations based on Fick's law cannot be solved without specific boundary conditions.
Parameter information from nonlinear cosmological fields
Watts, A T P
2000-01-01
We develop a general formalism for analysing parameter information from non-Gaussian cosmic fields. The method can be adapted to include the nonlinear effects in galaxy redshift surveys, weak lensing surveys and cosmic velocity field surveys as part of parameter estimation. It can also be used as a test of non-Gaussianity of the Cosmic Microwave Background. Generalising Maximum Likelihood analysis to second-order, we calculate the nonlinear Fisher Information matrix and likelihood surfaces in parameter space. To this order we find that the information content is always increased by including nonlinearity. Our methods are applied to a realistic model of a galaxy redshift survey, including nonlinear evolution, galaxy bias, shot-noise and redshift-space distortions to second-order. We find that including nonlinearities allows all of the degeneracies between parameters to be lifted. Marginalised parameter uncertainties of a few percent will then be obtainable using forthcoming galaxy redshift surveys.
Statistical distribution of nonlinear random wave height
Institute of Scientific and Technical Information of China (English)
HOU; Yijun; GUO; Peifang; SONG; Guiting; SONG; Jinbao; YIN; Baoshu; ZHAO; Xixi
2006-01-01
A statistical model of random wave is developed using Stokes wave theory of water wave dynamics. A new nonlinear probability distribution function of wave height is presented. The results indicate that wave steepness not only could be a parameter of the distribution function of wave height but also could reflect the degree of wave height distribution deviation from the Rayleigh distribution. The new wave height distribution overcomes the problem of Rayleigh distribution that the prediction of big wave is overestimated and the general wave is underestimated. The prediction of small probability wave height value of new distribution is also smaller than that of Rayleigh distribution. Wave height data taken from East China Normal University are used to verify the new distribution. The results indicate that the new distribution fits the measurements much better than the Rayleigh distribution.
Curvatures for Parameter Subsets in Nonlinear Regression
1986-01-01
The relative curvature measures of nonlinearity proposed by Bates and Watts (1980) are extended to an arbitrary subset of the parameters in a normal, nonlinear regression model. In particular, the subset curvatures proposed indicate the validity of linearization-based approximate confidence intervals for single parameters. The derivation produces the original Bates-Watts measures directly from the likelihood function. When the intrinsic curvature is negligible, the Bates-Watts parameter-effec...
Distributed Parameter Modelling Applications
DEFF Research Database (Denmark)
2011-01-01
Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers and the d......Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers...... sands processing. The fertilizer granulation model considers the dynamics of MAP-DAP (mono and diammonium phosphates) production within an industrial granulator, that involves complex crystallisation, chemical reaction and particle growth, captured through population balances. A final example considers...
Non-Linear Logging Parameters Inversion
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The non-linear logging parameters inversion is based on the field theory, information optimization and predication theory. It uses seismic charaoters,geological model and logging data as a restriction to inverse 2D, 3D logging parameters data volume. Using this method,
Approximating parameters in nonlinear reaction diffusion equations
Directory of Open Access Journals (Sweden)
Robert R. Ferdinand
2001-07-01
Full Text Available We present a model describing population dynamics in an environment. The model is a nonlinear, nonlocal, reaction diffusion equation with Neumann boundary conditions. An inverse method, involving minimization of a least-squares cost functional, is developed to identify unknown model parameters. Finally, numerical results are presented which display estimates of these parameters using computationally generated data.
varying elastic parameters distributions
Moussawi, Ali
2014-12-01
The experimental identication of mechanical properties is crucial in mechanics for understanding material behavior and for the development of numerical models. Classical identi cation procedures employ standard shaped specimens, assume that the mechanical elds in the object are homogeneous, and recover global properties. Thus, multiple tests are required for full characterization of a heterogeneous object, leading to a time consuming and costly process. The development of non-contact, full- eld measurement techniques from which complex kinematic elds can be recorded has opened the door to a new way of thinking. From the identi cation point of view, suitable methods can be used to process these complex kinematic elds in order to recover multiple spatially varying parameters through one test or a few tests. The requirement is the development of identi cation techniques that can process these complex experimental data. This thesis introduces a novel identi cation technique called the constitutive compatibility method. The key idea is to de ne stresses as compatible with the observed kinematic eld through the chosen class of constitutive equation, making possible the uncoupling of the identi cation of stress from the identi cation of the material parameters. This uncoupling leads to parametrized solutions in cases where 5 the solution is non-unique (due to unknown traction boundary conditions) as demonstrated on 2D numerical examples. First the theory is outlined and the method is demonstrated in 2D applications. Second, the method is implemented within a domain decomposition framework in order to reduce the cost for processing very large problems. Finally, it is extended to 3D numerical examples. Promising results are shown for 2D and 3D problems.
Distribution of the nonlinear random ocean wave period
Institute of Scientific and Technical Information of China (English)
HOU Yijun; LI Mingjie; SONG Guiting; SI Guangcheng; QI Peng; HU Po
2009-01-01
Because of the intrinsic difficulty in determining distributions for wave periods, previous studies on wave period distribution models have not taken nonlinearity into account and have not performed well in terms of describing and statistically analyzing the probability density distribution of ocean waves. In this study, a statistical model of random waves is developed using Stokes wave theory of water wave dynamics. In addition, a new nonlinear probability distribution function for the wave period is presented with the parameters of spectral density width and nonlinear wave steepness, which is more reasonable as a physical mechanism. The magnitude of wave steepness determines the intensity of the nonlinear effect, while the spectral width only changes the energy distribution. The wave steepness is found to be an important parameter in terms of not only dynamics but also statistics. The value of wave steepness reflects the degree that the wave period distribution skews from the Cauchy distribution, and it also describes the variation in the distribution function, which resembles that of the wave surface elevation distribution and wave height distribution. We found that the distribution curves skew leftward and upward as the wave steepness increases. The wave period observations for the SZFII-1 buoy, made off the coast of Weihai (37°27.6′ N, 122°15.1′ E), China, are used to verify the new distribution. The coefficient of the correlation between the new distribution and the buoy data at different spectral widths (υ=0.3-0.5) is within the range of 0.968 6 to 0.991 7. In addition, the Longuet-Higgins (1975) and Sun (1988) distributions and the new distribution presented in this work are compared. The validations and comparisons indicate that the new nonlinear probability density distribution fits the buoy measurements better than the Longuet-Higgins and Sun distributions do. We believe that adoption of the new wave period distribution would improve traditional
PARAMETER ESTIMATION OF EXPONENTIAL DISTRIBUTION
Institute of Scientific and Technical Information of China (English)
XU Haiyan; FEI Heliang
2005-01-01
Because of the importance of grouped data, many scholars have been devoted to the study of this kind of data. But, few documents have been concerned with the threshold parameter. In this paper, we assume that the threshold parameter is smaller than the first observing point. Then, on the basis of the two-parameter exponential distribution, the maximum likelihood estimations of both parameters are given, the sufficient and necessary conditions for their existence and uniqueness are argued, and the asymptotic properties of the estimations are also presented, according to which approximate confidence intervals of the parameters are derived. At the same time, the estimation of the parameters is generalized, and some methods are introduced to get explicit expressions of these generalized estimations. Also, a special case where the first failure time of the units is observed is considered.
Parameters Approach Applied on Nonlinear Oscillators
Directory of Open Access Journals (Sweden)
Najeeb Alam Khan
2014-01-01
Full Text Available We applied an approach to obtain the natural frequency of the generalized Duffing oscillator u¨ + u + α3u3 + α5u5 + α7u7 + ⋯ + αnun=0 and a nonlinear oscillator with a restoring force which is the function of a noninteger power exponent of deflection u¨+αu|u|n−1=0. This approach is based on involved parameters, initial conditions, and collocation points. For any arbitrary power of n, the approximate frequency analysis is carried out between the natural frequency and amplitude. The solution procedure is simple, and the results obtained are valid for the whole solution domain.
Parameter Identification of Weakly Nonlinear Vibration System in Frequency Domain
Directory of Open Access Journals (Sweden)
Jiehua Peng
2004-01-01
Full Text Available A new method of identifying parameters of nonlinearly vibrating system in frequency domain is presented in this paper. The problems of parameter identification of the nonlinear dynamic system with nonlinear elastic force or nonlinear damping force are discussed. In the method, the mathematic model of parameter identification is frequency response function. Firstly, by means of perturbation method the frequency response function of weakly nonlinear vibration system is derived. Next, a parameter transformation is made and the frequency response function becomes a linear function of the new parameters. Then, based on this function and with the least square method, physical parameters of the system are identified. Finally, the applicability of the proposed technique is confirmed by numerical simulation.
Crystalline structure and symmetry dependence of acoustic nonlinearity parameters
Cantrell, John H.
1994-01-01
A quantitative measure of elastic wave nonlinearity in crystals is provided by the acoustic nonlinearity parameters. The nonlinearity parameters are defined for arbitrary propagation modes for solids of arbitrary crystalline symmetry and are determined along the pure mode propagation directions for 33 crystals of cubic symmetry from data reported in the literature. The magnitudes of the nonlinearity parameters are found to exhibit a strong dependence on the crystalline structure and symmetries associated with the modal direction in the solid. Calculations based on the Born-Mayer potential for crystals having a dominant repulsive contribution to the elastic constants from the interatomic pair potential suggest that the origin of the structure dependence is associated with the shape rather than the strength of the potential. Considerations based on variations in crystal symmetry during loading along pure mode propagation directions of face-centered-cubic solids provide a qualitative explanation for the dependence of the acoustic nonlinearity parameters on modal direction.
Statistical distribution of nonlinear random wave height in shallow water
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
Here we present a statistical model of random wave,using Stokes wave theory of water wave dynamics,as well as a new nonlinear probability distribution function of wave height in shallow water.It is more physically logical to use the wave steepness of shallow water and the factor of shallow water as the parameters in the wave height distribution.The results indicate that the two parameters not only could be parameters of the distribution function of wave height but also could reflect the degree of wave height distribution deviation from the Rayleigh distribution.The new wave height distribution overcomes the problem of Rayleigh distribution that the prediction of big wave is overestimated and the general wave is underestimated.The prediction of small probability wave height value of new distribution is also smaller than that of Rayleigh distribution.The effect of wave steepness in shallow water is similar to that in deep water;but the factor of shallow water lowers the wave height distribution of the general wave with the reduced factor of wave steepness.It also makes the wave height distribution of shallow water more centralized.The results indicate that the new distribution fits the in situ measurements much better than other distributions.
Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input
Institute of Scientific and Technical Information of China (English)
张克勤; 庄开宇; 苏宏业; 褚健; 高红
2002-01-01
This paper presents a sliding mode(SM) based identifier to deal with the parameter idenfification problem for a class of parameter uncertain nonlinear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonlinear system;an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.
IDENTIFICATION OF PARAMETERS IN PARABOLIC EQUATIONS WITH NONLINEARITY
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
In this paper, we consider the identification of parameters in parabolic equations with nonlinearity. Some approximation processes for the identification problem are given. Our results improve and generalize the previous results.
Multiple nonlinear parameter estimation using PI feedback control
Lith, van P. F.; Witteveen, H.; Betlem, B.H.L.; Roffel, B.
2001-01-01
Nonlinear parameters often need to be estimated during the building of chemical process models. To accomplish this, many techniques are available. This paper discusses an alternative view to parameter estimation, where the concept of PI feedback control is used to estimate model parameters. The appr
Nonlinear Progressive Collapse Analysis Including Distributed Plasticity
Mohamed Osama Ahmed; Imam Zubair Syed; Khattab Rania
2016-01-01
This paper demonstrates the effect of incorporating distributed plasticity in nonlinear analytical models used to assess the potential for progressive collapse of steel framed regular building structures. Emphasis on this paper is on the deformation response under the notionally removed column, in a typical Alternate Path (AP) method. The AP method employed in this paper is based on the provisions of the Unified Facilities Criteria – Design of Buildings to Resist Progressive Collapse, develop...
Linear and nonlinear degenerate abstract differential equations with small parameter
Shakhmurov, Veli B.
2016-01-01
The boundary value problems for linear and nonlinear regular degenerate abstract differential equations are studied. The equations have the principal variable coefficients and a small parameter. The linear problem is considered on a parameter-dependent domain (i.e., on a moving domain). The maximal regularity properties of linear problems and the optimal regularity of the nonlinear problem are obtained. In application, the well-posedness of the Cauchy problem for degenerate parabolic equation...
Some nonlinear parameters of PP intervals of pulse main peaks
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The PP intervals of pulse main peaks from healthy and unhealthy people (arrhythmia) have different nonlinear characteristics. In this paper, the extraction of PP intervals of pulse main peaks is achieved by picking up P peaks of pulse wave with wavelet transform. Furthermore, several nonlinear parameters (correlative dimensions, maximum Lyapunov exponents, complexity and approximate entropy) of the PP intervals of pulse main peaks extracted from normal and unhealthy pulse signals are calculated, with the results showing that these nonlinear parameters calculated from the main wave interval signals are helpful for analyzing human's health state and diagnosing heart diseases.
Bayesian parameter estimation for nonlinear modelling of biological pathways
Directory of Open Access Journals (Sweden)
Ghasemi Omid
2011-12-01
Full Text Available Abstract Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC method. We applied this approach to the biological pathways involved in the left ventricle (LV response to myocardial infarction (MI and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly
Galerkin approximation for inverse problems for nonautonomous nonlinear distributed systems
Banks, H. T.; Reich, Simeon; Rosen, I. G.
1988-01-01
An abstract framework and convergence theory is developed for Galerkin approximation for inverse problems involving the identification of nonautonomous nonlinear distributed parameter systems. A set of relatively easily verified conditions is provided which are sufficient to guarantee the existence of optimal solutions and their approximation by a sequence of solutions to a sequence of approximating finite dimensional identification problems. The approach is based on the theory of monotone operators in Banach spaces and is applicable to a reasonably broad class of nonlinear distributed systems. Operator theoretic and variational techniques are used to establish a fundamental convergence result. An example involving evolution systems with dynamics described by nonstationary quasilinear elliptic operators along with some applications are presented and discussed.
Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects
2017-02-22
AFRL-AFOSR-UK-TR-2017-0023 Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects Marco Martorella... UNIVERSITY DI PISA, DEPARTMENT DI INGEGNERIA Final Report 02/22/2017 DISTRIBUTION A: Distribution approved for public release. AF Office Of Scientific Research...Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-14-1-0183 5c. PROGRAM
Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input
Institute of Scientific and Technical Information of China (English)
张克勤; 庄开宇; 苏宏业; 褚健; 高红
2002-01-01
This paper presents a sliding mode (SM) based identifier to deal wit h the parameter identification problem for a class of parameter uncertain nonlin ear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonline ar system; an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.
Nonlinear Progressive Collapse Analysis Including Distributed Plasticity
Directory of Open Access Journals (Sweden)
Mohamed Osama Ahmed
2016-01-01
Full Text Available This paper demonstrates the effect of incorporating distributed plasticity in nonlinear analytical models used to assess the potential for progressive collapse of steel framed regular building structures. Emphasis on this paper is on the deformation response under the notionally removed column, in a typical Alternate Path (AP method. The AP method employed in this paper is based on the provisions of the Unified Facilities Criteria – Design of Buildings to Resist Progressive Collapse, developed and updated by the U.S. Department of Defense [1]. The AP method is often used for to assess the potential for progressive collapse of building structures that fall under Occupancy Category III or IV. A case study steel building is used to examine the effect of incorporating distributed plasticity, where moment frames were used on perimeter as well as the interior of the three dimensional structural system. It is concluded that the use of moment resisting frames within the structural system will enhance resistance to progressive collapse through ductile deformation response and that it is conserative to ignore the effects of distributed plasticity in determining peak displacement response under the notionally removed column.
Measurement of the Acoustic Nonlinearity Parameter for Biological Media.
Cobb, Wesley Nelson
In vitro measurements of the acoustic nonlinearity parameter are presented for several biological media. With these measurements it is possible to predict the distortion of a finite amplitude wave in biological tissues of current diagnostic and research interest. The measurement method is based on the finite amplitude distortion of a sine wave that is emmitted by a piston source. The growth of the second harmonic component of this wave is measured by a piston receiver which is coaxial with and has the same size as the source. The experimental measurements and theory are compared in order to determine the nonlinearity parameter. The density, sound speed, and attenuation for the medium are determined in order to make this comparison. The theory developed for this study accounts for the influence of both diffraction and attenuation on the experimental measurements. The effects of dispersion, tissue inhomogeneity and gas bubbles within the excised tissues are studied. To test the measurement method, experimental results are compared with established values for the nonlinearity parameter of distilled water, ethylene glycol and glycerol. The agreement between these values suggests that the measurement uncertainty is (+OR-) 5% for liquids and (+OR-) 10% for solid tissues. Measurements are presented for dog blood and bovine serum albumen as a function of concentration. The nonlinearity parameters for liver, kidney and spleen are reported for both human and canine tissues. The values for the fresh tissues displayed little variation (6.8 to 7.8). Measurements for fixed, normal and cirrhotic tissues indicated that the nonlinearity parameter does not depend strongly on pathology. However, the values for fixed tissues were somewhat higher than those of the fresh tissues.
Parton Distributions in Impact Parameter Space
Dahiya, H; Ray, S
2007-01-01
Fourier transform of the generalized parton distributions (GPDs) at zero skewness with respect to the transverse momentum transfer gives the distribution of partons in the impact parameter space. We investigate the GPDs as well as the impact parameter dependent parton distributions (ipdpdfs) by expressing them in terms of overlaps of light front wave functions (LFWFs) and present a comparative study using three different model LFWFs.
Parameter Estimation in Multivariate Gamma Distribution
V S Vaidyanathan; R Vani Lakshmi
2015-01-01
Multivariate gamma distribution finds abundant applications in stochastic modelling, hydrology and reliability. Parameter estimation in this distribution is a challenging one as it involves many parameters to be estimated simultaneously. In this paper, the form of multivariate gamma distribution proposed by Mathai and Moschopoulos [10] is considered. This form has nice properties in terms of marginal and conditional densities. A new method of estimation based on optimal search is proposed for...
Prediction of ventricular fibrillation based on nonlinear multi-parameter
Institute of Scientific and Technical Information of China (English)
SI Junfeng; NING Xinbao; ZHOU Lingling; ZHANG Song
2003-01-01
Ventricular fibrillation (VF) caused by myocardial ischemia is one of the leading factors of death attributed to cardiovascular diseases. It is particularly significant to predict VF and gain valuable time for clinic therapy. Fivedogs are taken as the research objects and a VF model is introduced. The nonlinear characteristics of the ECGs before and after VF are investigated with nonlinear multi-parame- ter analysis methods, Gaussian kernel (GK) correlation estimation algorithm and Lyapunov exponent estimation algorithm. Correlation entropy h2is also presented. The results indicate that there are three parameters which will change at the same time with the conditions of myocardial ischemia, and any changes of a single parameter may be caused by other factors and mislead the judgment. Multi-parameter analysis is more reliable to reveal the heart conditions,and to predict VF without misjudgments.
Parameter Estimation in Multivariate Gamma Distribution
Directory of Open Access Journals (Sweden)
V S Vaidyanathan
2015-05-01
Full Text Available Multivariate gamma distribution finds abundant applications in stochastic modelling, hydrology and reliability. Parameter estimation in this distribution is a challenging one as it involves many parameters to be estimated simultaneously. In this paper, the form of multivariate gamma distribution proposed by Mathai and Moschopoulos [10] is considered. This form has nice properties in terms of marginal and conditional densities. A new method of estimation based on optimal search is proposed for estimating the parameters using the marginal distributions and the concepts of maximum likelihood, spacings and least squares. The proposed methodology is easy to implement and is free from calculus. It optimizes the objective function by searching over a wide range of values and determines the estimate of the parameters. The consistency of the estimates is demonstrated in terms of mean, standard deviation and mean square error through simulation studies for different choices of parameters.
Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models
Bates, Bryson C.
1992-01-01
A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.
Inference for ordered parameters in multinomial distributions
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
This paper discusses inference for ordered parameters of multinomial distributions. We first show that the asymptotic distributions of their maximum likelihood estimators (MLEs) are not always normal and the bootstrap distribution estimators of the MLEs can be inconsistent. Then a class of weighted sum estimators (WSEs) of the ordered parameters is proposed. Properties of the WSEs are studied, including their asymptotic normality. Based on those results, large sample inferences for smooth functions of the ordered parameters can be made. Especially, the confidence intervals of the maximum cell probabilities are constructed. Simulation results indicate that this interval estimation performs much better than the bootstrap approaches in the literature. Finally, the above results for ordered parameters of multinomial distributions are extended to more general distribution models.
Weakly nonlinear dynamics and the σ{sub 8} parameter
Energy Technology Data Exchange (ETDEWEB)
Juszkiewicz, Roman [Department of Physics and Astronomy, Zielona Góra University, Lubuska 2, 65-265 Zielona Góra (Poland); Feldman, Hume A. [Department of Physics and Astronomy, University of Kansas, Lawrence KS 66045 (United States); Fry, J.N. [Department of Physics, University of Florida, Gainesville FL 32611-8440 (United States); Jaffe, Andrew H., E-mail: roman@camk.edu.pl, E-mail: feldman@ku.edu, E-mail: fry@phys.ufl.edu, E-mail: a.jaffe@imperial.ac.uk [Astrophysics, Blackett Laboratory, Imperial College, London SW7 2AZ (United Kingdom)
2010-02-01
The amplitude of cosmological density fluctuations, σ{sub 8}, has been studied and estimated by analysing many cosmological observations. The values of the estimates vary considerably between the various probes. However, different estimators probe the value of σ{sub 8} in different cosmological scales and do not take into account the nonlinear evolution of the parameter at late times. We show that estimates of the amplitude of cosmological density fluctuations derived from cosmic flows are systematically higher than those inferred at early epochs from the CMB because of nonlinear evolution at later times. We discuss the past and future evolution of linear and nonlinear perturbations, derive corrections to the value of σ{sub 8} and compare amplitudes after accounting for these differences.
Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation
Directory of Open Access Journals (Sweden)
Maja Marasović
2017-01-01
Full Text Available Accurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, is very important in modern biology. To this date, linearization of kinetic equations is still widely established practice for determining these parameters in chemical and enzyme catalysis. Although simplicity of linear optimization is alluring, these methods have certain pitfalls due to which they more often then not result in misleading estimation of enzyme parameters. In order to obtain more accurate predictions of parameter values, the use of nonlinear least-squares fitting techniques is recommended. However, when there are outliers present in the data, these techniques become unreliable. This paper proposes the use of a robust nonlinear regression estimator based on modified Tukey’s biweight function that can provide more resilient results in the presence of outliers and/or influential observations. Real and synthetic kinetic data have been used to test our approach. Monte Carlo simulations are performed to illustrate the efficacy and the robustness of the biweight estimator in comparison with the standard linearization methods and the ordinary least-squares nonlinear regression. We then apply this method to experimental data for the tyrosinase enzyme (EC 1.14.18.1 extracted from Solanum tuberosum, Agaricus bisporus, and Pleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values of Km and Vmax.
A generalization of the power law distribution with nonlinear exponent
Prieto, Faustino; Sarabia, José María
2017-01-01
The power law distribution is usually used to fit data in the upper tail of the distribution. However, commonly it is not valid to model data in all the range. In this paper, we present a new family of distributions, the so-called Generalized Power Law (GPL), which can be useful for modeling data in all the range and possess power law tails. To do that, we model the exponent of the power law using a non-linear function which depends on data and two parameters. Then, we provide some basic properties and some specific models of that new family of distributions. After that, we study a relevant model of the family, with special emphasis on the quantile and hazard functions, and the corresponding estimation and testing methods. Finally, as an empirical evidence, we study how the debt is distributed across municipalities in Spain. We check that power law model is only valid in the upper tail; we show analytically and graphically the competence of the new model with municipal debt data in the whole range; and we compare the new distribution with other well-known distributions including the Lognormal, the Generalized Pareto, the Fisk, the Burr type XII and the Dagum models.
Institute of Scientific and Technical Information of China (English)
郭苏; 刘德有; 张耀明; 许昌; 王沛
2014-01-01
直接蒸汽发电(direct steam generation，DSG)槽式太阳能热发电系统的集热器长度一般很长，且具有明显的分布参数特征。因此，建立DSG槽式太阳能集热器的非线性分布参数模型，以DSG集热器入口工质温度、质量流量和出口压力为边界条件，采用迎风格式的有限差分法对模型进行离散求解。仿真研究了DSG集热器主要参数在太阳辐射强度、给水温度和给水流量变化等扰动工况下的响应特性，结果与文献实验结果基本一致，验证了模型的正确性。结果表明：太阳辐射强度降低时，出口工质温度下降得很快；给水流量或给水温度小幅下降时，出口工质温度和流量都会滞后响应且变化显著；DSG 集热器出口工质流量在某些情况下会发生脉动，实际应用中应避免脉动状态的发生或降低其影响。%Direct steam generation (DSG) in parabolic trough solar power system has long solar collector and obvious distributed parameter characteristics. Therefore, a nonlinear distributed parameter model for parabolic trough DSG solar collectors was built in this paper. As a boundary condition, fluid temperature and mass flow had to be provided at the inlet as well as the pressure at the outlet, and the finite differential approach with an upwind scheme was adopted to discrete and solve the model. Compared with experimental results from the literature, the correctness of the model was validated by simulation results during the main conditions such as solar radiation intensity, inlet fluid temperature and inlet mass flow change. Simulation results show that fluid temperature at the outlet decreases quickly when solar radiation intensity is declined; Furthermore, the responses of fluid temperature and mass flow at the outlet delay largely and stabilize slowly when mass flow or temperature at the inlet declined slightly; Most significantly, a pulsation phenomenon of outlet mass flow may happen
Distributed Synchronization Control of Multiagent Systems With Unknown Nonlinearities.
Su, Shize; Lin, Zongli; Garcia, Alfredo
2016-01-01
This paper revisits the distributed adaptive control problem for synchronization of multiagent systems where the dynamics of the agents are nonlinear, nonidentical, unknown, and subject to external disturbances. Two communication topologies, represented, respectively, by a fixed strongly-connected directed graph and by a switching connected undirected graph, are considered. Under both of these communication topologies, we use distributed neural networks to approximate the uncertain dynamics. Decentralized adaptive control protocols are then constructed to solve the cooperative tracker problem, the problem of synchronization of all follower agents to a leader agent. In particular, we show that, under the proposed decentralized control protocols, the synchronization errors are ultimately bounded, and their ultimate bounds can be reduced arbitrarily by choosing the control parameter appropriately. Simulation study verifies the effectiveness of our proposed protocols.
Riccati-parameter solutions of nonlinear second-order ODEs
Energy Technology Data Exchange (ETDEWEB)
Reyes, M A [Instituto de Fisica, Universidad de Guanajuato, Leon, Guanajuato (Mexico); Rosu, H C [PotosIInstitute of Science and Technology, Apdo Postal 3-74 Tangamanga, 78231 San Luis PotosI (Mexico)], E-mail: hcr@ipicyt.edu.mx
2008-07-18
It has been proven by Rosu and Cornejo-Perez (Rosu and Cornejo-Perez 2005 Phys. Rev. E 71 046607, Cornejo-Perez and Rosu 2005 Prog. Theor. Phys. 114 533) that for some nonlinear second-order ODEs it is a very simple task to find one particular solution once the nonlinear equation is factorized with the use of two first-order differential operators. Here, it is shown that an interesting class of parametric solutions is easy to obtain if the proposed factorization has a particular form, which happily turns out to be the case in many problems of physical interest. The method that we exemplify with a few explicitly solved cases consists in using the general solution of the Riccati equation, which contributes with one parameter to this class of parametric solutions. For these nonlinear cases, the Riccati parameter serves as a 'growth' parameter from the trivial null solution up to the particular solution found through the factorization procedure.
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
Directory of Open Access Journals (Sweden)
muhammad zahid rashid
2011-04-01
Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR, moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
Directory of Open Access Journals (Sweden)
muhammad zahid rashid
2011-04-01
Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR, moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes
Nonlinear Optical Parameters of Magnetoactive Semiconductor-Plasmas
Singh, M.; Joseph, D.; Duhan, S.
The nonlinear optical parameters (absorption coefficient and refractive index) of semiconductor-plasmas subjected to a transverse magnetic field have been investigated analytically. By employing the coupled-mode scheme, an expression of third-order optical susceptibility and resultant nonlinear absorption and refractive index of the medium are obtained. The analysis has been applied to both cases, viz., centrosymmetric (β = 0) and noncentrosymmetric (β ≠ 0) in the presence of magnetic field. The numerical estimates are made for InSb crystal at liquid nitrogen temperature duly irradiated by a 10-nanosecond pulsed 10.6 μm CO2 laser. The influence of doping concentration and magnetic field on both the nonlinear absorption and refractive index has been explored, and the results are found to be well in agreement with theory and experiment. Analysis further establishes that absorption coefficient and refractive index can be controlled with precision in semiconductors by the proper selection of doping concentration and an external magnetic field, and hence these media may be used for fabrication of fast cubic nonlinear optical devices under off-resonant transition regime.
Directory of Open Access Journals (Sweden)
Yan Che
2012-01-01
Full Text Available The estimation problem is investigated for a class of stochastic nonlinear systems with distributed time-varying delays and missing measurements. The considered distributed time-varying delays, stochastic nonlinearities, and missing measurements are modeled in random ways governed by Bernoulli stochastic variables. The discussed nonlinearities are expressed by the statistical means. By using the linear matrix inequality method, a sufficient condition is established to guarantee the mean-square stability of the estimation error, and then the estimator parameters are characterized by the solution to a set of LMIs. Finally, a simulation example is exploited to show the effectiveness of the proposed design procedures.
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems
Directory of Open Access Journals (Sweden)
Banga Julio R
2006-11-01
Full Text Available Abstract Background We consider the problem of parameter estimation (model calibration in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector. In order to surmount these difficulties, global optimization (GO methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown structure (i.e. black-box models. In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously
A new method for parameter estimation in nonlinear dynamical equations
Wang, Liu; He, Wen-Ping; Liao, Le-Jian; Wan, Shi-Quan; He, Tao
2015-01-01
Parameter estimation is an important scientific problem in various fields such as chaos control, chaos synchronization and other mathematical models. In this paper, a new method for parameter estimation in nonlinear dynamical equations is proposed based on evolutionary modelling (EM). This will be achieved by utilizing the following characteristics of EM which includes self-organizing, adaptive and self-learning features which are inspired by biological natural selection, and mutation and genetic inheritance. The performance of the new method is demonstrated by using various numerical tests on the classic chaos model—Lorenz equation (Lorenz 1963). The results indicate that the new method can be used for fast and effective parameter estimation irrespective of whether partial parameters or all parameters are unknown in the Lorenz equation. Moreover, the new method has a good convergence rate. Noises are inevitable in observational data. The influence of observational noises on the performance of the presented method has been investigated. The results indicate that the strong noises, such as signal noise ratio (SNR) of 10 dB, have a larger influence on parameter estimation than the relatively weak noises. However, it is found that the precision of the parameter estimation remains acceptable for the relatively weak noises, e.g. SNR is 20 or 30 dB. It indicates that the presented method also has some anti-noise performance.
Multivariate distributions of soil hydraulic parameters
Qu, Wei; Pachepsky, Yakov; Huisman, Johan Alexander; Martinez, Gonzalo; Bogena, Heye; Vereecken, Harry
2014-05-01
Statistical distributions of soil hydraulic parameters have to be known when synthetic fields of soil hydraulic properties need to be generated in ensemble modeling of soil water dynamics and soil water content data assimilation. Pedotransfer functions that provide statistical distributions of water retention and hydraulic conductivity parameters for textural classes are most often used in the parameter field generation. Presence of strong correlations can substantially influence the parameter generation results. The objective of this work was to review and evaluate available data on correlations between van Genuchten-Mualem (VGM) model parameters. So far, two different approaches were developed to estimate these correlations. The first approach uses pedotransfer functions to generate VGM parameters for a large number of soil compositions within a textural class, and then computes parameter correlations for each of the textural classes. The second approach computes the VGM parameter correlations directly from parameter values obtained by fitting VGM model to measured water retention and hydraulic conductivity data for soil samples belonging to a textural class. Carsel and Parish (1988) used the Rawls et al. (1982) pedotransfer functions, and Meyer et al. (1997) used the Rosetta pedotransfer algorithms (Schaap, 2002) to develop correlations according to the first approach. We used the UNSODA database (Nemes et al. 2001), the US Southern Plains database (Timlin et al., 1999), and the Belgian database (Vereecken et al., 1989, 1990) to apply the second approach. A substantial number of considerable (>0.7) correlation coefficients were found. Large differences were encountered between parameter correlations obtained with different approaches and different databases for the same textural classes. The first of the two approaches resulted in generally higher values of correlation coefficients between VGM parameters. However, results of the first approach application depend
Density Matrix for Mesoscopic Distributed Parameter Circuits
Institute of Scientific and Technical Information of China (English)
JI Ying-Hua; WANG Qi; LUO Hai-Mei; LEI Min-Sheng
2005-01-01
Under the Born-von-Karmann periodic boundary condition, we propose a quantization scheme for nondissipative distributed parameter circuits (i.e. a uniform periodic transmission line). We find the unitary operator for diagonalizing the Hamiltonian of the uniform periodic transmission line. The unitary operator is expressed in a coordinate representation that brings convenience to deriving the density matrix p(q, q',β). The quantum fluctuations of charge and current at a definite temperature have been studied. It is shown that quantum fluctuations of distributed parameter circuits, which also have distributed properties, are related to both the circuit parameters and the positions and the mode of signals and temperature T. The higher the temperature is, the stronger quantum noise the circuit exhibits.
Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.
Sobhani-Tehrani, E; Talebi, H A; Khorasani, K
2014-02-01
This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements.
Parameter estimation of a nonlinear magnetic universe from observations
Montiel, Ariadna; Salzano, Vincenzo
2014-01-01
The cosmological model consisting of a nonlinear magnetic field obeying the Lagrangian L= \\gamma F^{\\alpha}, F being the electromagnetic invariant, coupled to a Robertson-Walker geometry is tested with observational data of Type Ia Supernovae, Long Gamma-Ray Bursts and Hubble parameter measurements. The statistical analysis show that the inclusion of nonlinear electromagnetic matter is enough to produce the observed accelerated expansion, with not need of including a dark energy component. The electromagnetic matter with abundance $\\Omega_B$, gives as best fit from the combination of all observational data sets \\Omega_B=0.562^{+0.037}_{-0.038} for the scenario in which \\alpha=-1, \\Omega_B=0.654^{+0.040}_{-0.040} for the scenario with \\alpha=-1/4 and \\Omega_B=0.683^{+0.039}_{-0.043} for the one with \\alpha=-1/8. These results indicate that nonlinear electromagnetic matter could play the role of dark energy, with the theoretical advantage of being a mensurable field.
Compositional modelling of distributed-parameter systems
Maschke, Bernhard; Schaft, van der Arjan; Lamnabhi-Lagarrigue, F.; Loría, A.; Panteley, E.
2005-01-01
The Hamiltonian formulation of distributed-parameter systems has been a challenging reserach area for quite some time. (A nice introduction, especially with respect to systems stemming from fluid dynamics, can be found in [26], where also a historical account is provided.) The identification of the
Prediction and simulation errors in parameter estimation for nonlinear systems
Aguirre, Luis A.; Barbosa, Bruno H. G.; Braga, Antônio P.
2010-11-01
This article compares the pros and cons of using prediction error and simulation error to define cost functions for parameter estimation in the context of nonlinear system identification. To avoid being influenced by estimators of the least squares family (e.g. prediction error methods), and in order to be able to solve non-convex optimisation problems (e.g. minimisation of some norm of the free-run simulation error), evolutionary algorithms were used. Simulated examples which include polynomial, rational and neural network models are discussed. Our results—obtained using different model classes—show that, in general the use of simulation error is preferable to prediction error. An interesting exception to this rule seems to be the equation error case when the model structure includes the true model. In the case of error-in-variables, although parameter estimation is biased in both cases, the algorithm based on simulation error is more robust.
PARAMETER ESTIMATION METHODOLOGY FOR NONLINEAR SYSTEMS: APPLICATION TO INDUCTION MOTOR
Institute of Scientific and Technical Information of China (English)
G.KENNE; F.FLORET; H.NKWAWO; F.LAMNABHI-LAGARRIGUE
2005-01-01
This paper deals with on-line state and parameter estimation of a reasonably large class of nonlinear continuous-time systems using a step-by-step sliding mode observer approach. The method proposed can also be used for adaptation to parameters that vary with time. The other interesting feature of the method is that it is easily implementable in real-time. The efficiency of this technique is demonstrated via the on-line estimation of the electrical parameters and rotor flux of an induction motor. This application is based on the standard model of the induction motor expressed in rotor coordinates with the stator current and voltage as well as the rotor speed assumed to be measurable.Real-time implementation results are then reported and the ability of the algorithm to rapidly estimate the motor parameters is demonstrated. These results show the robustness of this approach with respect to measurement noise, discretization effects, parameter uncertainties and modeling inaccuracies.Comparisons between the results obtained and those of the classical recursive least square algorithm are also presented. The real-time implementation results show that the proposed algorithm gives better performance than the recursive least square method in terms of the convergence rate and the robustness with respect to measurement noise.
Parameter Estimation Technique of Nonlinear Prosthetic Hand System
Directory of Open Access Journals (Sweden)
M.H.Jali
2016-10-01
Full Text Available This paper illustrated the parameter estimation technique of motorized prosthetic hand system. Prosthetic hands have become importance device to help amputee to gain a normal functional hand. By integrating various types of actuators such as DC motor, hydraulic and pneumatic as well as mechanical part, a highly useful and functional prosthetic device can be produced. One of the first steps to develop a prosthetic device is to design a control system. Mathematical modeling is derived to ease the control design process later on. This paper explained the parameter estimation technique of a nonlinear dynamic modeling of the system using Lagrangian equation. The model of the system is derived by considering the energies of the finger when it is actuated by the DC motor. The parameter estimation technique is implemented using Simulink Design Optimization toolbox in MATLAB. All the parameters are optimized until it achieves a satisfactory output response. The results show that the output response of the system with parameter estimation value produces a better response compare to the default value
Optimal control of nonsmooth distributed parameter systems
Tiba, Dan
1990-01-01
The book is devoted to the study of distributed control problems governed by various nonsmooth state systems. The main questions investigated include: existence of optimal pairs, first order optimality conditions, state-constrained systems, approximation and discretization, bang-bang and regularity properties for optimal control. In order to give the reader a better overview of the domain, several sections deal with topics that do not enter directly into the announced subject: boundary control, delay differential equations. In a subject still actively developing, the methods can be more important than the results and these include: adapted penalization techniques, the singular control systems approach, the variational inequality method, the Ekeland variational principle. Some prerequisites relating to convex analysis, nonlinear operators and partial differential equations are collected in the first chapter or are supplied appropriately in the text. The monograph is intended for graduate students and for resea...
Design for robust stabilization of nonlinear systems with uncertain parameters
Institute of Scientific and Technical Information of China (English)
赖旭芝; 文静; 吴敏
2004-01-01
Based on Lyapunov stability theory, a design method for the robust stabilization problem of a class of nonlinear systems with uncertain parameters is presented. The design procedure is divided into two steps: the first is to design controllers for the nominal system and make the system asymptotically stabilize at the expected equilibrium point; the second is to construct closed-loop nominal system based on the first step, then design robust controller to make the error of state between the original system and the nominal system converge to zero, thereby a dynamic controller with the constructed closed-loop nominal system served as interior dynamic is obtained. A numerical simulation verifies the correctness of the design method.
Nonlinear genetic-based simulation of soil shear strength parameters
Indian Academy of Sciences (India)
Seyyed Mohammad Mousavi; Amir Hossein Alavi; Amir Hossein Gandomi; Ali Mollahasani
2011-12-01
New nonlinear solutions were developed to estimate the soil shear strength parameters utilizing linear genetic programming (LGP). The soil cohesion intercept () and angle of shearing resistance () were formulated in terms of the basic soil physical properties. The best models were selected after developing and controlling several models with different combinations of influencing parameters. Comprehensive experimental database used for developing the models was established upon a series of unconsolidated, undrained, and unsaturated triaxial tests conducted in this study. Further, sensitivity and parametric analyses were carried out. and were found to be mostly influenced by the soil unit weight and liquid limit. In order to benchmark the proposed models, a multiple least squares regression (MLSR) analysis was performed. The validity of the models was proved on portions of laboratory results that were not included in the modelling process. The developed models are able to effectively learn the complex relationship between the soil strength parameters and their contributing factors. The LGP models provide a significantly better prediction performance than the regression models.
Parameter Estimation of Nonlinear Systems by Dynamic Cuckoo Search.
Liao, Qixiang; Zhou, Shudao; Shi, Hanqing; Shi, Weilai
2017-04-01
In order to address with the problem of the traditional or improved cuckoo search (CS) algorithm, we propose a dynamic adaptive cuckoo search with crossover operator (DACS-CO) algorithm. Normally, the parameters of the CS algorithm are kept constant or adapted by empirical equation that may result in decreasing the efficiency of the algorithm. In order to solve the problem, a feedback control scheme of algorithm parameters is adopted in cuckoo search; Rechenberg's 1/5 criterion, combined with a learning strategy, is used to evaluate the evolution process. In addition, there are no information exchanges between individuals for cuckoo search algorithm. To promote the search progress and overcome premature convergence, the multiple-point random crossover operator is merged into the CS algorithm to exchange information between individuals and improve the diversification and intensification of the population. The performance of the proposed hybrid algorithm is investigated through different nonlinear systems, with the numerical results demonstrating that the method can estimate parameters accurately and efficiently. Finally, we compare the results with the standard CS algorithm, orthogonal learning cuckoo search algorithm (OLCS), an adaptive and simulated annealing operation with the cuckoo search algorithm (ACS-SA), a genetic algorithm (GA), a particle swarm optimization algorithm (PSO), and a genetic simulated annealing algorithm (GA-SA). Our simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
A Novel Nonlinear Programming Model for Distribution Protection Optimization
Zambon, Eduardo; Bossois, Débora Z.; Garcia, Berilhes B.; Azeredo, Elias F.
2009-01-01
This paper presents a novel nonlinear binary programming model designed to improve the reliability indices of a distribution network. This model identifies the type and location of protection devices that should be installed in a distribution feeder and is a generalization of the classical optimizat
Active control and parameter updating techniques for nonlinear thermal network models
Papalexandris, M. V.; Milman, M. H.
The present article reports on active control and parameter updating techniques for thermal models based on the network approach. Emphasis is placed on applications where radiation plays a dominant role. Examples of such applications are the thermal design and modeling of spacecrafts and space-based science instruments. Active thermal control of a system aims to approximate a desired temperature distribution or to minimize a suitably defined temperature-dependent functional. Similarly, parameter updating aims to update the values of certain parameters of the thermal model so that the output approximates a distribution obtained through direct measurements. Both problems are formulated as nonlinear, least-square optimization problems. The proposed strategies for their solution are explained in detail and their efficiency is demonstrated through numerical tests. Finally, certain theoretical results pertaining to the characterization of solutions of the problems of interest are also presented.
Lee, C.-H.; Herget, C. J.
1976-01-01
This short paper considers the parameter-identification problem of general discrete-time, nonlinear, multiple input-multiple output dynamic systems with Gaussian white distributed measurement errors. Knowledge of the system parameterization is assumed to be available. Regions of constrained maximum likelihood (CML) parameter identifiability are established. A computation procedure employing interval arithmetic is proposed for finding explicit regions of parameter identifiability for the case of linear systems.
A nonlinear theory of tensor distributions
Vickers, J A
1998-01-01
The coordinate invariant theory of generalised functions of Colombeau and Meril is reviewed and extended to enable the construction of multi-index generalised tensor functions whose transformation laws coincide with their counterparts in classical distribution theory.
Distribution Development for STORM Ingestion Input Parameters
Energy Technology Data Exchange (ETDEWEB)
Fulton, John [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-07-01
The Sandia-developed Transport of Radioactive Materials (STORM) code suite is used as part of the Radioisotope Power System Launch Safety (RPSLS) program to perform statistical modeling of the consequences due to release of radioactive material given a launch accident. As part of this modeling, STORM samples input parameters from probability distributions with some parameters treated as constants. This report described the work done to convert four of these constant inputs (Consumption Rate, Average Crop Yield, Cropland to Landuse Database Ratio, and Crop Uptake Factor) to sampled values. Consumption rate changed from a constant value of 557.68 kg / yr to a normal distribution with a mean of 102.96 kg / yr and a standard deviation of 2.65 kg / yr. Meanwhile, Average Crop Yield changed from a constant value of 3.783 kg edible / m ^{2} to a normal distribution with a mean of 3.23 kg edible / m ^{2} and a standard deviation of 0.442 kg edible / m ^{2} . The Cropland to Landuse Database ratio changed from a constant value of 0.0996 (9.96%) to a normal distribution with a mean value of 0.0312 (3.12%) and a standard deviation of 0.00292 (0.29%). Finally the crop uptake factor changed from a constant value of 6.37e^{-4} (Bq crop /kg)/(Bq soil /kg) to a lognormal distribution with a geometric mean value of 3.38e^{-4} (Bq crop /kg)/(Bq soil /kg) and a standard deviation value of 3.33 (Bq crop /kg)/(Bq soil /kg)
Parameter Scaling in Non-Linear Microwave Tomography
DEFF Research Database (Denmark)
Jensen, Peter Damsgaard; Rubæk, Tonny; Talcoth, Oskar;
2012-01-01
Non-linear microwave tomographic imaging of the breast is a challenging computational problem. The breast is heterogeneous and contains several high-contrast and lossy regions, resulting in large differences in the measured signal levels. This implies that special care must be taken when the imag......Non-linear microwave tomographic imaging of the breast is a challenging computational problem. The breast is heterogeneous and contains several high-contrast and lossy regions, resulting in large differences in the measured signal levels. This implies that special care must be taken when...... the imaging problem is formulated. Under such conditions, microwave imaging systems will most often be considerably more sensitive to changes in the electromagnetic properties in certain regions of the breast. The result is that the parameters might not be reconstructed correctly in the less sensitive regions...... introduced as a measure of the sensitivity. The scaling of the parameters is shown to improve performance of the microwave imaging system when applied to reconstruction of images from 2-D simulated data and measurement data....
Parameter Scaling in Non-Linear Microwave Tomography
DEFF Research Database (Denmark)
Jensen, Peter Damsgaard; Rubæk, Tonny; Talcoth, Oskar
2012-01-01
Non-linear microwave tomographic imaging of the breast is a challenging computational problem. The breast is heterogeneous and contains several high-contrast and lossy regions, resulting in large differences in the measured signal levels. This implies that special care must be taken when the imag......Non-linear microwave tomographic imaging of the breast is a challenging computational problem. The breast is heterogeneous and contains several high-contrast and lossy regions, resulting in large differences in the measured signal levels. This implies that special care must be taken when...... the imaging problem is formulated. Under such conditions, microwave imaging systems will most often be considerably more sensitive to changes in the electromagnetic properties in certain regions of the breast. The result is that the parameters might not be reconstructed correctly in the less sensitive regions...... introduced as a measure of the sensitivity. The scaling of the parameters is shown to improve performance of the microwave imaging system when applied to reconstruction of images from 2-D simulated data and measurement data....
Distributed parameter statics of magnetic catheters.
Tunay, Ilker
2011-01-01
We discuss how to use special Cosserat rod theory for deriving distributed-parameter static equilibrium equations of magnetic catheters. These medical devices are used for minimally-invasive diagnostic and therapeutic procedures and can be operated remotely or controlled by automated algorithms. The magnetic material can be lumped in rigid segments or distributed in flexible segments. The position vector of the cross-section centroid and quaternion representation of an orthonormal triad are selected as DOF. The strain energy for transversely isotropic, hyperelastic rods is augmented with the mechanical potential energy of the magnetic field and a penalty term to enforce the quaternion unity constraint. Numerical solution is found by 1D finite elements. Material properties of polymer tubes in extension, bending and twist are determined by mechanical and magnetic experiments. Software experiments with commercial FEM software indicate that the computational effort with the proposed method is at least one order of magnitude less than standard 3D FEM.
Assigning probability distributions to input parameters of performance assessment models
Energy Technology Data Exchange (ETDEWEB)
Mishra, Srikanta [INTERA Inc., Austin, TX (United States)
2002-02-01
This study presents an overview of various approaches for assigning probability distributions to input parameters and/or future states of performance assessment models. Specifically,three broad approaches are discussed for developing input distributions: (a) fitting continuous distributions to data, (b) subjective assessment of probabilities, and (c) Bayesian updating of prior knowledge based on new information. The report begins with a summary of the nature of data and distributions, followed by a discussion of several common theoretical parametric models for characterizing distributions. Next, various techniques are presented for fitting continuous distributions to data. These include probability plotting, method of moments, maximum likelihood estimation and nonlinear least squares analysis. The techniques are demonstrated using data from a recent performance assessment study for the Yucca Mountain project. Goodness of fit techniques are also discussed, followed by an overview of how distribution fitting is accomplished in commercial software packages. The issue of subjective assessment of probabilities is dealt with in terms of the maximum entropy distribution selection approach, as well as some common rules for codifying informal expert judgment. Formal expert elicitation protocols are discussed next, and are based primarily on the guidance provided by the US NRC. The Bayesian framework for updating prior distributions (beliefs) when new information becomes available is discussed. A simple numerical approach is presented for facilitating practical applications of the Bayes theorem. Finally, a systematic framework for assigning distributions is presented: (a) for the situation where enough data are available to define an empirical CDF or fit a parametric model to the data, and (b) to deal with the situation where only a limited amount of information is available.
Institute of Scientific and Technical Information of China (English)
DONG Sheng; LI Fengli; JIAO Guiying
2003-01-01
Hydrologic frequency analysis plays an important role in coastal and ocean engineering for structural design and disaster prevention in coastal areas. This paper proposes a Nonlinear Least Squares Method (NLSM), which estimates the three unknown parameters of the Weibull distribution simultaneously by an iteration method. Statistical test shows that the NLSM fits each data sample well. The effects of different parameter-fitting methods, distribution models, and threshold values are also discussed in the statistical analysis of storm set-down elevation. The best-fitting probability distribution is given and the corresponding return values are estimated for engineering design.
Robust receding horizon control for networked and distributed nonlinear systems
Li, Huiping
2017-01-01
This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear dynamics, system constraints, and disturbances arising in networked and large-scale systems and which can be widely applied. These receding-horizon-control-based strategies can achieve sub-optimal control performance while ensuring closed-loop stability: a feature attractive to engineers. The authors address the problems of networked and distributed control step-by-step, gradually increasing the level of challenge presented. The book first introduces the state-feedback control problems of nonlinear networked systems and then studies output feedback control problems. For large-scale nonlinear systems, disturbance is considered first, then communication delay separately, and lastly the simultaneous combination of delays and disturbances. Each chapter of this easy-to-follow book not only proposes and analyzes novel ...
Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.
Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua
2016-11-14
In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.
Distributed Extreme Learning Machine for Nonlinear Learning over Network
Directory of Open Access Journals (Sweden)
Songyan Huang
2015-02-01
Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.
Similarities Derived from 3-D Nonlinear Psychophysics: Variance Distributions.
Gregson, Robert A. M.
1994-01-01
The derivation of the variance of similarity judgments is made from the 3-D process in nonlinear psychophysics. The idea of separability of dimensions in metric space theories of similarity is replaced by one parameter that represents the degree of a form of interdimensional cross-sampling. (SLD)
Measurement of ultrasonic nonlinear parameter by using electromagnetic acoustic transducer
Cai, Zhichao; Liu, Suzhen; Zhang, Chuang
2017-02-01
The nonlinear ultrasonic technology is generally known as an effective method for the microcrack detection. However, most of the previous experimental studies were limited by a contact nonlinearity method. Since measurement by the contact method is affected by the coupling conditions, additional nonlinear coefficient are lead into the measurement. This research presents a novel technique for nonlinear ultrasonic wave measurements that uses a non-contact electromagnetic ultrasonic transducer (EMAT). And for a better understanding and a more in-depth analysis of the macroscopic nonlinear behavior of microcrack, the developed FEM modeling approach was built to simulate microcrack induced nonlinearities manifested in electromagnetic ultrasonic waves and validated experimentally. This study has yielded a quantitative characterization strategy for microcrack using EMAT, facilitating deployment of structural health monitoring by noncontact electromagnetic nondestructive testing.
Non-linear equation: energy conservation and impact parameter dependence
Kormilitzin, Andrey
2010-01-01
In this paper we address two questions: how energy conservation affects the solution to the non-linear equation, and how impact parameter dependence influences the inclusive production. Answering the first question we solve the modified BK equation which takes into account energy conservation. In spite of the fact that we used the simplified kernel, we believe that the main result of the paper: the small ($\\leq 40%$) suppression of the inclusive productiondue to energy conservation, reflects a general feature. This result leads us to believe that the small value of the nuclear modification factor is of a non-perturbative nature. In the solution a new scale appears $Q_{fr} = Q_s \\exp(-1/(2 \\bas))$ and the production of dipoles with the size larger than $2/Q_{fr}$ is suppressed. Therefore, we can expect that the typical temperature for hadron production is about $Q_{fr}$ ($ T \\approx Q_{fr}$). The simplified equation allows us to obtain a solution to Balitsky-Kovchegov equation taking into account the impact pa...
Weyl geometry and the nonlinear mechanics of distributed point defects
Yavari, A.
2012-09-05
The residual stress field of a nonlinear elastic solid with a spherically symmetric distribution of point defects is obtained explicitly using methods from differential geometry. The material manifold of a solid with distributed point defects-where the body is stress-free-is a flat Weyl manifold, i.e. a manifold with an affine connection that has non-metricity with vanishing traceless part, but both its torsion and curvature tensors vanish. Given a spherically symmetric point defect distribution, we construct its Weyl material manifold using the method of Cartan\\'s moving frames. Having the material manifold, the anelasticity problem is transformed to a nonlinear elasticity problem and reduces the problem of computing the residual stresses to finding an embedding into the Euclidean ambient space. In the case of incompressible neo-Hookean solids, we calculate explicitly this residual stress field. We consider the example of a finite ball and a point defect distribution uniform in a smaller ball and vanishing elsewhere. We show that the residual stress field inside the smaller ball is uniform and hydrostatic. We also prove a nonlinear analogue of Eshelby\\'s celebrated inclusion problem for a spherical inclusion in an isotropic incompressible nonlinear solid. © 2012 The Royal Society.
The Nonlinear Sigma Model With Distributed Adaptive Mesh Refinement
Liebling, S L
2004-01-01
An adaptive mesh refinement (AMR) scheme is implemented in a distributed environment using Message Passing Interface (MPI) to find solutions to the nonlinear sigma model. Previous work studied behavior similar to black hole critical phenomena at the threshold for singularity formation in this flat space model. This work is a follow-up describing extensions to distribute the grid hierarchy and presenting tests showing the correctness of the model.
Distributed Extreme Learning Machine for Nonlinear Learning over Network
Songyan Huang; Chunguang Li
2015-01-01
Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN) with radial basis function (RBF) hidden neurons has the ability to approximate any continuous functions and, thus, may be used as...
Grieco, Andrew Lewis
Precise characterization of waveguide parameters is necessary for the successful design of nonlinear photonic devices. This dissertation contains a description of methods for the experimental characterization of distributed Bragg reflectors for use in nonlinear optics and other applications. The general coupled-mode theory of Bragg reflection arising from a periodic dielectric perturbation is developed from Maxwell's equations. This theory is then applied to develop a method of characterizing the fundamental parameters that describe Bragg reflection by comparing the spectral response of Bragg reflector resonators. This method is also extended to characterize linear loss in waveguides. A model of nonlinear effects in Bragg reflector resonators manifesting in bistability is also developed, as this phenomenon can be detrimental to the characterization method. Specific recommendations are made regarding waveguide fabrication and experimental design to reduce sources of experimental error.
Non-linearity parameter / of binary liquid mixtures at elevated pressures
Indian Academy of Sciences (India)
J D Pandey; J Chhabra; R Dey; V Sanguri; R Verma
2000-09-01
When sound waves of high amplitude propagate, several non-linear effects occur. Ultrasonic studies in liquid mixtures provide valuable information about structure and interaction in such systems. The present investigation comprises of theoretical evaluation of the acoustic non-linearity parameter / of four binary liquid mixtures using Tong and Dong equation at high pressures and = 303.15 K. Thermodynamic method has also been used to calculate the non-linearity parameter after making certain approximations.
Design of nonlinear discrete-time controllers using a parameter space sampling procedure
Young, G. E.; Auslander, D. M.
1983-01-01
The design of nonlinear discrete-time controllers is investigated where the control algorithm assumes a special form. State-dependent control actions are obtained from tables whose values are the design parameters. A new design methodology capable of dealing with nonlinear systems containing parameter uncertainty is used to obtain the controller design. Various controller strategies are presented and illustrated through an example.
An extended harmonic balance method based on incremental nonlinear control parameters
Khodaparast, Hamed Haddad; Madinei, Hadi; Friswell, Michael I.; Adhikari, Sondipon; Coggon, Simon; Cooper, Jonathan E.
2017-02-01
A new formulation for calculating the steady-state responses of multiple-degree-of-freedom (MDOF) non-linear dynamic systems due to harmonic excitation is developed. This is aimed at solving multi-dimensional nonlinear systems using linear equations. Nonlinearity is parameterised by a set of 'non-linear control parameters' such that the dynamic system is effectively linear for zero values of these parameters and nonlinearity increases with increasing values of these parameters. Two sets of linear equations which are formed from a first-order truncated Taylor series expansion are developed. The first set of linear equations provides the summation of sensitivities of linear system responses with respect to non-linear control parameters and the second set are recursive equations that use the previous responses to update the sensitivities. The obtained sensitivities of steady-state responses are then used to calculate the steady state responses of non-linear dynamic systems in an iterative process. The application and verification of the method are illustrated using a non-linear Micro-Electro-Mechanical System (MEMS) subject to a base harmonic excitation. The non-linear control parameters in these examples are the DC voltages that are applied to the electrodes of the MEMS devices.
Relationship between the temperature and the acoustic nonlinearity parameter in biological tissues
Institute of Scientific and Technical Information of China (English)
LU Ying; LIU Xiaozhou; GONG Xiufen; ZHANG Dong
2004-01-01
Recently with the rapid development of the high-intensity focused ultrasound (HIFU) in biomedical ultrasound, much attention has been paid to the noninvasive temperature estimation in biological tissue in order to determine the region and degree of the ultrasound-induced lesions. In ultrasound hyperthermal therapy it is highly desirable to study the real-time noninvasive monitoring of temperature distribution in biological tissue. In this paper, the relationship between the nonlinearity parameter B/A and the temperature in biological tissue is studied and compared with the theoretical model as well as the experimental results from the thermocouple. Results indicated that B/A could be used as an effective tool to monitor the temperature distribution in biological media.
Hirose, Hideo
1998-01-01
TYPES OF THE DISTRIBUTION:13;Normal distribution (2-parameter)13;Uniform distribution (2-parameter)13;Exponential distribution ( 2-parameter)13;Weibull distribution (2-parameter)13;Gumbel Distribution (2-parameter)13;Weibull/Frechet Distribution (3-parameter)13;Generalized extreme-value distribution (3-parameter)13;Gamma distribution (3-parameter)13;Extended Gamma distribution (3-parameter)13;Log-normal distribution (3-parameter)13;Extended Log-normal distribution (3-parameter)13;Generalized ...
Hirose, Hideo
1998-01-01
TYPES OF THE DISTRIBUTION:13;Normal distribution (2-parameter)13;Uniform distribution (2-parameter)13;Exponential distribution ( 2-parameter)13;Weibull distribution (2-parameter)13;Gumbel Distribution (2-parameter)13;Weibull/Frechet Distribution (3-parameter)13;Generalized extreme-value distribution (3-parameter)13;Gamma distribution (3-parameter)13;Extended Gamma distribution (3-parameter)13;Log-normal distribution (3-parameter)13;Extended Log-normal distribution (3-parameter)13;Generalized ...
Liu, Jingwei; Liu, Yi; Xu, Meizhi
2015-01-01
Parameter estimation method of Jelinski-Moranda (JM) model based on weighted nonlinear least squares (WNLS) is proposed. The formulae of resolving the parameter WNLS estimation (WNLSE) are derived, and the empirical weight function and heteroscedasticity problem are discussed. The effects of optimization parameter estimation selection based on maximum likelihood estimation (MLE) method, least squares estimation (LSE) method and weighted nonlinear least squares estimation (WNLSE) method are al...
Control and Estimation of Distributed Parameter Systems
Kappel, F; Kunisch, K
1998-01-01
Consisting of 23 refereed contributions, this volume offers a broad and diverse view of current research in control and estimation of partial differential equations. Topics addressed include, but are not limited to - control and stability of hyperbolic systems related to elasticity, linear and nonlinear; - control and identification of nonlinear parabolic systems; - exact and approximate controllability, and observability; - Pontryagin's maximum principle and dynamic programming in PDE; and - numerics pertinent to optimal and suboptimal control problems. This volume is primarily geared toward control theorists seeking information on the latest developments in their area of expertise. It may also serve as a stimulating reader to any researcher who wants to gain an impression of activities at the forefront of a vigorously expanding area in applied mathematics.
Nonlinear wave propagation studies, dispersion modeling, and signal parameters correction
Czech Academy of Sciences Publication Activity Database
Převorovský, Zdeněk
..: ..., 2004, 00. [European Workshop on FP6-AERONEWS /1./. Naples (IT), 13.09.2004-16.09.2004] EU Projects: European Commission(XE) 502927 - AERO-NEWS Institutional research plan: CEZ:AV0Z2076919 Keywords : nodestructive testing * nonlinear elastic wave spectroscopy Subject RIV: BI - Acoustics
Parameters for efficient growth of second harmonic field in nonlinear photonic crystals
Energy Technology Data Exchange (ETDEWEB)
Joseph, Shereena, E-mail: sherin5462@gmail.com; Khan, Mohd. Shahid; Hafiz, Aurangzeb Khurram
2014-03-01
The ultrashort pulse propagation and nonlinear second harmonic generation under the undepleted pump approximation in a quadratic nonlinear photonic crystal (NPC) structure is theoretically investigated and the optimized parameters for high second harmonic generation conversion efficiency are extracted. The transfer matrix method is used for the numerical formulation for oblique angle of incidence. A unique set of material combination GaInP/InAlP is selected as alternating nonlinear and linear layers. The NPC parameters like incident angle and layer thickness are manipulated to obtain the exact phase matching using double resonance condition for a fixed number of layers with known experimental material parameters.
Institute of Scientific and Technical Information of China (English)
TAO Hua-xue; GUO Jin-yun
2005-01-01
The unknown parameter's variance-covariance propagation and calculation in the generalized nonlinear least squares remain to be studied now,which didn't appear in the internal and external referencing documents. The unknown parameter's variance-covariance propagation formula, considering the two-power terms, was concluded used to evaluate the accuracy of unknown parameter estimators in the generalized nonlinear least squares problem. It is a new variance-covariance formula and opens up a new way to evaluate the accuracy when processing data which have the multi-source,multi-dimensional, multi-type, multi-time-state, different accuracy and nonlinearity.
A novel method for extracting acoustic nonlinearity parameters with diffraction corrections
Energy Technology Data Exchange (ETDEWEB)
Jeong, Hyunjo [Wonkwang University, Iksan (Korea, Republic of); Zhang, Shuzeng; Li, Xiongbing [Central South University, Changsha (China)
2016-02-15
A new method for determining the acoustic nonlinearity parameter using a nonlinear data fitting method is proposed. Based on the quasilinear theory of Westervelt's equation, the fundamental and second harmonic beam fields are expressed as a multi-Gaussian beam model that separates the attenuation and diffraction correction terms from the propagating plane waves. A nonlinear least squares curve fitting method is developed to extract the nonlinearity parameter without knowing the attenuation coefficients of the material being tested. The nonlinearity parameter of water is determined using the proposed method, and the result agrees well with the literature value. The attenuation coefficients of the fundamental and the second harmonic are also extracted and discussed.
Performance emulation and parameter estimation for nonlinear fibre-optic links
DEFF Research Database (Denmark)
Piels, Molly; Porto da Silva, Edson; Zibar, Darko
2016-01-01
Fibre-optic communication systems, especially when operating in the nonlinear regime, generally do not perform exactly as theory would predict. A number of methods for data-based evaluation of nonlinear fibre-optic link parameters, both for accurate performance emulation and optimization...
Performance emulation and parameter estimation for nonlinear fibre-optic links
DEFF Research Database (Denmark)
Piels, Molly; Porto da Silva, Edson; Zibar, Darko;
2016-01-01
Fibre-optic communication systems, especially when operating in the nonlinear regime, generally do not perform exactly as theory would predict. A number of methods for data-based evaluation of nonlinear fibre-optic link parameters, both for accurate performance emulation and optimization, are rev...
Zhao, Youxuan; Li, Feilong; Cao, Peng; Liu, Yaolu; Zhang, Jianyu; Fu, Shaoyun; Zhang, Jun; Hu, Ning
2017-08-01
Since the identification of micro-cracks in engineering materials is very valuable in understanding the initial and slight changes in mechanical properties of materials under complex working environments, numerical simulations on the propagation of the low frequency S0 Lamb wave in thin plates with randomly distributed micro-cracks were performed to study the behavior of nonlinear Lamb waves. The results showed that while the influence of the randomly distributed micro-cracks on the phase velocity of the low frequency S0 fundamental waves could be neglected, significant ultrasonic nonlinear effects caused by the randomly distributed micro-cracks was discovered, which mainly presented as a second harmonic generation. By using a Monte Carlo simulation method, we found that the acoustic nonlinear parameter increased linearly with the micro-crack density and the size of micro-crack zone, and it was also related to the excitation frequency and friction coefficient of the micro-crack surfaces. In addition, it was found that the nonlinear effect of waves reflected by the micro-cracks was more noticeable than that of the transmitted waves. This study theoretically reveals that the low frequency S0 mode of Lamb waves can be used as the fundamental waves to quantitatively identify micro-cracks in thin plates. Copyright © 2017 Elsevier B.V. All rights reserved.
Institute of Scientific and Technical Information of China (English)
Liu Xiao-Bei; Li Biao
2011-01-01
We present three families of soliton solutions to the generalized (3+1)-dimensional nonlinear Schr(o)dinger equation with distributed coefficients. We investigate the dynamics of these solitons in nonlinear optics with some selected parameters.Different shapes of bright solitons,a train of bright solitons and dark solitons are observed.The obtained results may raise the possibilities of relevant experiments and potential applications.
Ultra-large nonlinear parameter in graphene-silicon waveguide structures.
Donnelly, Christine; Tan, Dawn T H
2014-09-22
Mono-layer graphene integrated with optical waveguides is studied for the purpose of maximizing E-field interaction with the graphene layer, for the generation of ultra-large nonlinear parameters. It is shown that the common approach used to minimize the waveguide effective modal area does not accurately predict the configuration with the maximum nonlinear parameter. Both photonic and plasmonic waveguide configurations and graphene integration techniques realizable with today's fabrication tools are studied. Importantly, nonlinear parameters exceeding 10(4) W(-1)/m, two orders of magnitude larger than that in silicon on insulator waveguides without graphene, are obtained for the quasi-TE mode in silicon waveguides incorporating mono-layer graphene in the evanescent part of the optical field. Dielectric loaded surface plasmon polariton waveguides incorporating mono-layer graphene are observed to generate nonlinear parameters as large as 10(5) W(-1)/m, three orders of magnitude larger than that in silicon on insulator waveguides without graphene. The ultra-large nonlinear parameters make such waveguides promising platforms for nonlinear integrated optics at ultra-low powers, and for previously unobserved nonlinear optical effects to be studied in a waveguide platform.
Energy Technology Data Exchange (ETDEWEB)
Jeong, Hyun Jo; Cho, Sung Jong; Nam, Ki Woong; Lee, Jang Hyun [Division of Mechanical and Automotive Engineering, Wonkwang University, Iksan (Korea, Republic of)
2016-04-15
The nonlinearity parameter is frequently measured as a sensitive indicator in damaged material characterization or tissue harmonic imaging. Several previous studies have employed the plane wave solution, and ignored the effects of beam diffraction when measuring the non-linearity parameter β. This paper presents a multi-Gaussian beam approach to explicitly derive diffraction corrections for fundamental and second harmonics under quasilinear and paraxial approximation. Their effects on the nonlinearity parameter estimation demonstrate complicated dependence of β on the transmitter-receiver geometries, frequency, and propagation distance. The diffraction effects on the non-linearity parameter estimation are important even in the nearfield region. Experiments are performed to show that improved β values can be obtained by considering the diffraction effects.
Hays, J. R.
1969-01-01
Lumped parametric system models are simplified and computationally advantageous in the frequency domain of linear systems. Nonlinear least squares computer program finds the least square best estimate for any number of parameters in an arbitrarily complicated model.
Simulating the Effect of Non-Linear Mode-Coupling in Cosmological Parameter Estimation
Kiessling, A; Heavens, A F
2011-01-01
Fisher Information Matrix methods are commonly used in cosmology to estimate the accuracy that cosmological parameters can be measured with a given experiment, and to optimise the design of experiments. However, the standard approach usually assumes both data and parameter estimates are Gaussian-distributed. Further, for survey forecasts and optimisation it is usually assumed the power-spectra covariance matrix is diagonal in Fourier-space. But in the low-redshift Universe, non-linear mode-coupling will tend to correlate small-scale power, moving information from lower to higher-order moments of the field. This movement of information will change the predictions of cosmological parameter accuracy. In this paper we quantify this loss of information by comparing naive Gaussian Fisher matrix forecasts with a Maximum Likelihood parameter estimation analysis of a suite of mock weak lensing catalogues derived from N-body simulations, based on the SUNGLASS pipeline, for a 2-D and tomographic shear analysis of a Eucl...
Institute of Scientific and Technical Information of China (English)
MA Qingyu; LU Rongrong; ZHANG Dong; GONG Xiufen; LIU Xiaozhou
2003-01-01
Measurement of nonlinearity parameter using the second-harmonic reflective model is studied. A new kind of compound transducer is designed and fabricated for this purpose. With this transducer and the finite amplitude insert-substitution method, an experimental system to measure the nonlinearity parameter using reflective model is developed. B/A values of some liquids and biological tissues are obtained and results coincide well with those presented in the literatures.
Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter
Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling
2017-07-01
The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.
Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter
Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling
2017-08-01
The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.
Estimation of Physical Parameters in Linear and Nonlinear Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten
and estimation of physical parameters in particular. 2. To apply the new methods for modelling of specific objects, such as loudspeakers, ac- and dc-motors wind turbines and beat exchangers. A reliable quality measure of an obtained parameter estimate is a prerequisite for any reasonable use of the result...
Evaluation of third order nonlinear optical parameters of CdS/PVA nanocomposite
Energy Technology Data Exchange (ETDEWEB)
Sharma, Mamta [Department of Physics, Center of Advanced Study in Physics, Panjab University, Chandigarh-160014 (India); Department of Applied Sciences (Physics), UIET, Panjab University, Chandigarh-160014 (India); Tripathi, S. K., E-mail: surya@pu.ac.in, E-mail: surya-tr@yahoo.com [Department of Physics, Center of Advanced Study in Physics, Panjab University, Chandigarh-160014 (India)
2015-06-24
CdS nanoparticles dispersed in PVA are prepared by Chemical method at room temperature. The nonlinear optical parameters such as nonlinear absorption (β), nonlinear refractive index (n{sub 2}) and nonlinear susceptibility (χ{sup 3}) are calculated for this sample by using Z-scan technique. CdS/PVA samples show the two photon absorption mechanism. The third order nonlinear susceptibility is calculated from n{sub 2} and β and is found to be of the order of 10{sup −7} – 10{sup −8} m{sup 2}/V{sup 2}. The larger value of third order nonlinear susceptibility is due to dielectric and quantum confinement effect.
Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen
2006-01-01
This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.
Cubic spline approximation techniques for parameter estimation in distributed systems
Banks, H. T.; Crowley, J. M.; Kunisch, K.
1983-01-01
Approximation schemes employing cubic splines in the context of a linear semigroup framework are developed for both parabolic and hyperbolic second-order partial differential equation parameter estimation problems. Convergence results are established for problems with linear and nonlinear systems, and a summary of numerical experiments with the techniques proposed is given.
On the Confidence Interval for the parameter of Poisson Distribution
Bityukov, S I; Taperechkina, V A
2000-01-01
In present paper the possibility of construction of continuous analogue of Poisson distribution with the search of bounds of confidence intervals for parameter of Poisson distribution is discussed and the results of numerical construction of confidence intervals are presented.
Likelihood Inference of Nonlinear Models Based on a Class of Flexible Skewed Distributions
Directory of Open Access Journals (Sweden)
Xuedong Chen
2014-01-01
Full Text Available This paper deals with the issue of the likelihood inference for nonlinear models with a flexible skew-t-normal (FSTN distribution, which is proposed within a general framework of flexible skew-symmetric (FSS distributions by combining with skew-t-normal (STN distribution. In comparison with the common skewed distributions such as skew normal (SN, and skew-t (ST as well as scale mixtures of skew normal (SMSN, the FSTN distribution can accommodate more flexibility and robustness in the presence of skewed, heavy-tailed, especially multimodal outcomes. However, for this distribution, a usual approach of maximum likelihood estimates based on EM algorithm becomes unavailable and an alternative way is to return to the original Newton-Raphson type method. In order to improve the estimation as well as the way for confidence estimation and hypothesis test for the parameters of interest, a modified Newton-Raphson iterative algorithm is presented in this paper, based on profile likelihood for nonlinear regression models with FSTN distribution, and, then, the confidence interval and hypothesis test are also developed. Furthermore, a real example and simulation are conducted to demonstrate the usefulness and the superiority of our approach.
Toman, Matej; Štumberger, Gorazd; Štumberger, Bojan; Dolinar, Drago
Power packages for calculation of power system transients are often used when studying and designing electromagnetic power systems. An accurate model of a distribution transformer is needed in order to obtain realistic values from these calculations. This transformer model must be derived in such a way that it is applicable when calculating those operating conditions appearing in practice. Operation conditions where transformers are loaded with nonlinear and unbalanced loads are especially challenging. The purpose of this work is to derive a three-phase transformer model that is appropriate for evaluating the effects of nonlinear and unbalanced loads. A lumped parameter model instead of a finite element (FE) model is considered in order to ensure that the model can be used in power packages for the calculation of power system transients. The transformer model is obtained by coupling electric and magnetic equivalent circuits. The magnetic equivalent circuit contains only three nonlinear reluctances, which represent nonlinear behaviour of the transformer. They are calculated by the inverse Jiles-Atherton (J-A) hysteresis model, while parameters of hysteresis are identified using differential evolution (DE). This considerably improves the accuracy of the derived transformer model. Although the obtained transformer model is simple, the simulation results show good agreement between measured and calculated results.
On Calculating the Hougaard Measure of Skewness in a Nonlinear Regression Model with Two Parameters
Directory of Open Access Journals (Sweden)
S. A. EL-Shehawy
2009-01-01
Full Text Available Problem statement: This study presented an alternative computational algorithm for determining the values of the Hougaard measure of skewness as a nonlinearity measure in a Nonlinear Regression model (NLR-model with two parameters. Approach: These values indicated a degree of a nonlinear behavior in the estimator of the parameter in a NLR-model. Results: We applied the suggested algorithm on an example of a NLR-model in which there is a conditionally linear parameter. The algorithm is mainly based on many earlier studies in measures of nonlinearity. The algorithm was suited for implementation using computer algebra systems such as MAPLE, MATLAB and MATHEMATICA. Conclusion/Recommendations: The results with the corresponding output the same considering example will be compared with the results in some earlier studies.
Directory of Open Access Journals (Sweden)
Shaolong Chen
2016-01-01
Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.
Wang, Gang; Wang, Chaoli; Du, Qinghui; Cai, Xuan
2016-10-01
In this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.
Optimal Parameter Tuning in a Predictive Nonlinear Control Method for a Mobile Robot
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D. Hazry
2006-01-01
Full Text Available This study contributes to a new optimal parameter tuning in a predictive nonlinear control method for stable trajectory straight line tracking with a non-holonomic mobile robot. In this method, the focus lies in finding the optimal parameter estimation and to predict the path that the mobile robot will follow for stable trajectory straight line tracking system. The stability control contains three parameters: 1 deflection parameter for the traveling direction of the mobile robot 2 deflection parameter for the distance across traveling direction of the mobile robot and 3 deflection parameter for the steering angle of the mobile robot . Two hundred and seventy three experimental were performed and the results have been analyzed and described herewith. It is found that by using a new optimal parameter tuning in a predictive nonlinear control method derived from the extension of kinematics model, the movement of the mobile robot is stabilized and adhered to the reference posture
Ying, L H
2012-01-01
Nonlinear instability and refraction by ocean currents are both important mechanisms that go beyond the Rayleigh approximation and may be responsible for the formation of freak waves. In this paper, we quantitatively study nonlinear effects on the evolution of surface gravity waves on the ocean, to explore systematically the effects of various input parameters on the probability of freak wave formation. The fourth-order current-modified nonlinear Schr\\"odinger equation (CNLS4) is employed to describe the wave evolution. By solving CNLS4 numerically, we are able to obtain quantitative predictions for the wave height distribution as a function of key environmental conditions such as average steepness, angular spread, and frequency spread of the local sea state. Additionally, we explore the spatial dependence of the wave height distribution, associated with the buildup of nonlinear development.
Estimation of Parameters of the Beta-Extreme Value Distribution
Directory of Open Access Journals (Sweden)
Zafar Iqbal
2008-09-01
Full Text Available In this research paper The Beta Extreme Value Type (III distribution which is developed by Zafar and Aleem (2007 is considered and parameters are estimated by using moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m’ & ‘n’ are real and moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m��� & ‘n’ are integers and then a Comparison between rth moments about origin when parameters are ‘m’ & ‘n’ are real and when parameters are ‘m’ & ‘n’ are integers. At the end second method, method of Maximum Likelihood is used to estimate the unknown parameters of the Beta Extreme Value Type (III distribution.
A Comparative Study of Distribution System Parameter Estimation Methods
Energy Technology Data Exchange (ETDEWEB)
Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup
2016-07-17
In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.
Measurement of the acoustic nonlinearity parameter B/A of lossy medium in a focused field
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
An analytical description for the linear and nonlinear acoustic fields in lossy medium of a focusing source is derived. The relationship of pressure amplitudes at focus for fundamental and the second harmonic waves is discussed. At high linear focusing gain G, a new method using the insert substitution method for measuring the acoustic nonlinear parameter B /A of biological tissues is presented. Results for some biological tissues are experimentally obtained.
A One-parameter Filled Function Method for Nonlinear Integer Programming
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
This paper gives a new definition of the filled function for nonlinear integer programming problem. A filled function satisfying our definition is presented. This function contains only one parameter. The properties of the proposed filled function and the method using this filled function to solve nonlinear integer programming problem are also discussed. Numerical results indicate the efficiency and reliability of the proposed filled function algorithm.
One-parameter quasi-filled function algorithm for nonlinear integer programming
Institute of Scientific and Technical Information of China (English)
SHANG You-lin; HAN Bo-shun
2005-01-01
A definition of the quasi-filled function for nonlinear integer programming problem is given in this paper. A quasi-filled function satisfying our definition is presented. This function contains only one parameter. The properties of the proposed quasi-filled function and the method using this quasi-filled function to solve nonlinear integer programming problem are also discussed in this paper. Numerical results indicated the efficiency and reliability of the proposed quasi-filled function algorithm.
Parameter estimation of stable distribution based on zero - order statistics
Chen, Jian; Chen, Hong; Cai, Xiaoxia; Weng, Pengfei; Nie, Hao
2017-08-01
With the increasing complexity of the channel, there are many impulse noise signals in the real channel. The statistical properties of such processes are significantly deviated from the Gaussian distribution, and the Alpha stable distribution provides a very useful theoretical tool for this process. This paper focuses on the parameter estimation method of the Alpha stable distribution. First, the basic theory of Alpha stable distribution is introduced. Then, the concept of logarithmic moment and geometric power are proposed. Finally, the parameter estimation of Alpha stable distribution is realized based on zero order statistic (ZOS). This method has better toughness and precision.
Institute of Scientific and Technical Information of China (English)
张家树; 肖先赐; 万继宏
2001-01-01
An adaptive nonlinear feedback-control method is proposed to control continuous-time chaotic dynamical systems,where the adaptive nonlinear controller acts on only one-dimensional error signals between the desired state and the observed chaotic state of a system. The reduced parameter adaptive quadratic predictor used in adaptive feedback cancellation of the nonlinear terms can control the system at any desired state. Computer simulation results on the Lorenz system are shown to demonstrate the effectiveness of this feedback-control method.
Wang, Luyun; Li, Lu; Li, Zhonghao; Zhou, Guosheng; Mihalache, Dumitru
2005-09-01
The generalized nonlinear Schrödinger model with distributed dispersion, nonlinearity, and gain or loss is considered and the explicit, analytical solutions describing the dynamics of bright solitons on a continuous-wave background are obtained in quadratures. Then, the generation, compression, and propagation of pulse trains are discussed in detail. The numerical results show that solitons can be compressed by choosing the appropriate control fiber system, and pulse trains generated by modulation instability can propagate undistorsted along fibers with distributed parameters by controlling appropriately the energy of each pulse in the pulse train.
Directory of Open Access Journals (Sweden)
Yin Dawei
2010-12-01
Full Text Available The estimation of aeroengine component deviation parameters (CDP is an important portion of aeronautical propulsion system performance-seeking control (PSC, which employs linear Kalman filter based on piecewise state variable model (SVM traditionally. But it’s not easy to get SVM, and the process of linearizing the nonlinear model to get the SVM will introduce errors. So parameters nonlinear estimation was introduced based on the nonlinear aeroengine model directly. The nonlinear estimation model is established according to aeroengine operation balance and the measured and calculated values matching of measurable parameters. The nonlinear estimation was changed to a problem of solving complex nonlinear equations, which is equal to an optimization problem. Time-varying inertia weight particle swarm optimization (PSO with constriction factor was employed to solve the problem in order to satisfy the requirement of precision and calculation speed. The simulation results of a given turbofan engine show that utilizing the improved PSO algorithm can estimate the CPD precisely with satisfied converging speed.
Stabilizing model predictive control for constrained nonlinear distributed delay systems.
Mahboobi Esfanjani, R; Nikravesh, S K Y
2011-04-01
In this paper, a model predictive control scheme with guaranteed closed-loop asymptotic stability is proposed for a class of constrained nonlinear time-delay systems with discrete and distributed delays. A suitable terminal cost functional and also an appropriate terminal region are utilized to achieve asymptotic stability. To determine the terminal cost, a locally asymptotically stabilizing controller is designed and an appropriate Lyapunov-Krasoskii functional of the locally stabilized system is employed as the terminal cost. Furthermore, an invariant set for locally stabilized system which is established by using the Razumikhin Theorem is used as the terminal region. Simple conditions are derived to obtain terminal cost and terminal region in terms of Bilinear Matrix Inequalities. The method is illustrated by a numerical example.
Wang, Chenliang; Wen, Changyun; Hu, Qinglei; Wang, Wei; Zhang, Xiuyu
2017-05-05
This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme.
Approximating electrical distribution networks via mixed-integer nonlinear programming
Energy Technology Data Exchange (ETDEWEB)
Lakhera, Sanyogita [Citibank, New York City, NY (United States); Shanbhag, Uday V. [Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Ave., Urbana, IL 61801 (United States); McInerney, Michael K. [Construction Engineering Research Laboratory (CERL) (United States)
2011-02-15
Given urban data derived from a geographical information system (GIS), we consider the problem of constructing an estimate of the electrical distribution system of an urban area. We employ the image data to obtain an approximate electrical load distribution over a network of a prespecificed discretization. Together with partial information about existing substations, we determine the optimal placement of electrical substations to sustain such a load that minimizes the cost of capital and losses. This requires solving large-scale quadratic programs with discrete variables for which we present a novel penalization-smoothing scheme. The choice of locations allows one to determine the optimal flows in this network, as required by physical requirements which provide us with an approximation of the distribution network. Furthermore, the scheme allows for approximating systems in the presence of no-go areas, such as lakes and fields. We examine the performance of our algorithm on the solution of a set of location problems and observe that the scheme is capable of solving large-scale instances, well beyond the realm of existing mixed-integer nonlinear programming solvers. We conclude with a case study in which a stage-wise extension of this scheme is developed to reflect the temporal evolution of load. (author)
Hong, Ming; Mao, Zhu; Todd, Michael D.; Su, Zhongqing
2017-01-01
Nonlinear features extracted from Lamb wave signals (e.g., second harmonic generation) are demonstrably sensitive to microscopic damage, such as fatigue and material thermal degradation. While a majority of the existing studies in this context is focused on detecting undersized damage in metallic materials, the present study is aimed at expanding such a detection philosophy to the domain of composites, by linking the relative acoustic nonlinearity parameter (RANP) - a prominent nonlinear signal feature of Lamb waves - to barely visible impact damage (BVID) in composites. Nevertheless, considering immense uncertainties inevitably embedded in acquired signals (due to instrumentation, environment, operation, computation/estimation, etc.) which can adversely obfuscate nonlinear features, it is necessary to quantify the uncertainty of the RANP (i.e., its statistics) in order to enhance decision-making associated with its use as a detection feature. A probabilistic model is established to numerically evaluate the statistical distribution of the RANP. Using piezoelectric wafers, Lamb waves are acquired and processed to produce histograms of RANP estimates in both the healthy and damaged conditions of a CF/EP laminate, to which the model is compared, with good agreement observed between the model-predicted and experimentally-obtained statistic distributions of the RANP. With the model, BVID in the laminate is predicted. The model is further made use of to quantify the level of confidence in damage prediction results based on the concept of a receiver operating characteristic, enabling the practitioners to better understand the obtained results in the presence of uncertainties.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization.We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.
Distributed Weighted Parameter Averaging for SVM Training on Big Data
Das, Ayan; Bhattacharya, Sourangshu
2015-01-01
Two popular approaches for distributed training of SVMs on big data are parameter averaging and ADMM. Parameter averaging is efficient but suffers from loss of accuracy with increase in number of partitions, while ADMM in the feature space is accurate but suffers from slow convergence. In this paper, we report a hybrid approach called weighted parameter averaging (WPA), which optimizes the regularized hinge loss with respect to weights on parameters. The problem is shown to be same as solving...
Estimation of Aircraft Nonlinear Unsteady Parameters From Wind Tunnel Data
Klein, Vladislav; Murphy, Patrick C.
1998-01-01
Aerodynamic equations were formulated for an aircraft in one-degree-of-freedom large amplitude motion about each of its body axes. The model formulation based on indicial functions separated the resulting aerodynamic forces and moments into static terms, purely rotary terms and unsteady terms. Model identification from experimental data combined stepwise regression and maximum likelihood estimation in a two-stage optimization algorithm that can identify the unsteady term and rotary term if necessary. The identification scheme was applied to oscillatory data in two examples. The model identified from experimental data fit the data well, however, some parameters were estimated with limited accuracy. The resulting model was a good predictor for oscillatory and ramp input data.
Estimation of growth parameters using a nonlinear mixed Gompertz model.
Wang, Z; Zuidhof, M J
2004-06-01
In order to maximize the utility of simulation models for decision making, accurate estimation of growth parameters and associated variances is crucial. A mixed Gompertz growth model was used to account for between-bird variation and heterogeneous variance. The mixed model had several advantages over the fixed effects model. The mixed model partitioned BW variation into between- and within-bird variation, and the covariance structure assumed with the random effect accounted for part of the BW correlation across ages in the same individual. The amount of residual variance decreased by over 55% with the mixed model. The mixed model reduced estimation biases that resulted from selective sampling. For analysis of longitudinal growth data, the mixed effects growth model is recommended.
New Fitting Formula for Cosmic Nonlinear Density Distribution
Shin, Jihye; Kim, Juhan; Pichon, Christophe; Jeong, Donghui; Park, Changbom
2017-07-01
We have measured the probability distribution function (PDF) of a cosmic matter density field from a suite of N-body simulations. We propose the generalized normal distribution of version 2 ({{ N }}{{v}2}) as an alternative fitting formula to the well-known log-normal distribution. We find that {{ N }}{{v}2} provides a significantly better fit than that of the log-normal distribution for all smoothing radii (2, 5, 10, 25 [Mpc h -1]) that we studied. The improvement is substantial in the underdense regions. The development of non-Gaussianities in the cosmic matter density field is captured by continuous evolution of the skewness and shift parameters of the {{ N }}{{v}2} distribution. We present the redshift evolution of these parameters for aforementioned smoothing radii and various background cosmology models. All the PDFs measured from large and high-resolution N-body simulations that we use in this study can be obtained from the web site https://astro.kias.re.kr/jhshin.
An improved method for nonlinear parameter estimation: a case study of the Rössler model
He, Wen-Ping; Wang, Liu; Jiang, Yun-Di; Wan, Shi-Quan
2016-08-01
Parameter estimation is an important research topic in nonlinear dynamics. Based on the evolutionary algorithm (EA), Wang et al. (2014) present a new scheme for nonlinear parameter estimation and numerical tests indicate that the estimation precision is satisfactory. However, the convergence rate of the EA is relatively slow when multiple unknown parameters in a multidimensional dynamical system are estimated simultaneously. To solve this problem, an improved method for parameter estimation of nonlinear dynamical equations is provided in the present paper. The main idea of the improved scheme is to use all of the known time series for all of the components in some dynamical equations to estimate the parameters in single component one by one, instead of estimating all of the parameters in all of the components simultaneously. Thus, we can estimate all of the parameters stage by stage. The performance of the improved method was tested using a classic chaotic system—Rössler model. The numerical tests show that the amended parameter estimation scheme can greatly improve the searching efficiency and that there is a significant increase in the convergence rate of the EA, particularly for multiparameter estimation in multidimensional dynamical equations. Moreover, the results indicate that the accuracy of parameter estimation and the CPU time consumed by the presented method have no obvious dependence on the sample size.
Effects of nonlinear strength parameters on stability of 3D soil slopes
Institute of Scientific and Technical Information of China (English)
高玉峰; 吴迪; 张飞; 秦红玉; 朱德胜
2016-01-01
Actual slope stability problems have three-dimensional (3D) characteristics and the soils of slopes have curved failure envelopes. This incorporates a power-law nonlinear failure criterion into the kinematic approach of limit analysis to conduct the evaluation of the stability of 3D slopes. A tangential technique is adopted to simplify the nonlinear failure criterion in the form of equivalent Mohr-Coulomb strength parameters. A class of 3D admissible rotational failure mechanisms is selected for soil slopes including three types of failure mechanisms: face failure, base failure, and toe failure. The upper-bound solutions and corresponding critical slip surfaces can be obtained by an efficient optimization method. The results indicate that the nonlinear parameters have significant influences on the assessment of slope stability, especially on the type of failure mechanism. The effects of nonlinear parameters appear to be pronounced for gentle slopes constrained to a narrow width. Compared with the solutions derived from plane-strain analysis, the 3D solutions are more sensitive to the values of nonlinear parameters.
Sun, Xiaodian; Jin, Li; Xiong, Momiao
2008-01-01
It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.
Solution of distributive problems with synthesis of radar information fields parameters
Directory of Open Access Journals (Sweden)
А. В. Нестеров
1999-05-01
Full Text Available Considered is the approach to solution of the problems of synthesis of radar and information fields parameters. It is proposed that, as the result of the synthesis, the structure of location of ground radar components should be determined. The optimal location of radar and information system is supposed to be determined by the results of the solution of the distribution problems. Considered are three sets of problems: method of linear programming, method of non-linear programming, method of scanning theory. Described are the distinctive features of each method, their advantages and disadvantages also the groups of determining parameters; an advice is given as to the use of particular approach
Nonlinear Sum Operator Equations with a Parameter and Application to Second-Order Three-Point BVPs
Directory of Open Access Journals (Sweden)
Wen-Xia Wang
2014-01-01
Full Text Available A class of nonlinear sum operator equations with a parameter on order Banach spaces were considered. The existence and uniqueness of positive solutions for this kind of operator equations and the dependence of solutions on the parameter have been obtained by using the properties of cone and nonlinear analysis methods. The critical value of the parameter was estimated. Further, the application to some nonlinear three-point boundary value problems was given to show the significance of the discussion.
Interval Estimations of the Two-Parameter Exponential Distribution
Directory of Open Access Journals (Sweden)
Lai Jiang
2012-01-01
Full Text Available In applied work, the two-parameter exponential distribution gives useful representations of many physical situations. Confidence interval for the scale parameter and predictive interval for a future independent observation have been studied by many, including Petropoulos (2011 and Lawless (1977, respectively. However, interval estimates for the threshold parameter have not been widely examined in statistical literature. The aim of this paper is to, first, obtain the exact significance function of the scale parameter by renormalizing the p∗-formula. Then the approximate Studentization method is applied to obtain the significance function of the threshold parameter. Finally, a predictive density function of the two-parameter exponential distribution is derived. A real-life data set is used to show the implementation of the method. Simulation studies are then carried out to illustrate the accuracy of the proposed methods.
On Estimating the Parameters of Truncated Trivariate Normal Distributions
Directory of Open Access Journals (Sweden)
M. N. Bhattacharyya
1969-07-01
Full Text Available Maximum likehood estimates of the parameters of a trivariate normal distribution, with single truncation on two-variates, have been derived in this paper. The information matrix has also been given from which the asymptotic variances and covariances might be obtained for the estimates of the parameters of the restricted variables. Numerical examples have been worked out.
Directory of Open Access Journals (Sweden)
Aijia Ouyang
2015-01-01
Full Text Available Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter θ to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if θ≠1/3, but interestingly when θ=1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.
National Aeronautics and Space Administration — The overall goal of the project is to develop reliable reduced order modeling technologies to automatically generate nonlinear, parameter-varying (PV),...
Distribution Line Parameter Estimation Under Consideration of Measurement Tolerances
DEFF Research Database (Denmark)
Prostejovsky, Alexander; Gehrke, Oliver; Kosek, Anna Magdalena
2016-01-01
State estimation and control approaches in electric distribution grids rely on precise electric models that may be inaccurate. This work presents a novel method of estimating distribution line parameters using only root mean square voltage and power measurements under consideration of measurement...
Sunbuloglu, Emin; Bozdag, Ergun; Toprak, Tuncer; Islak, Civan
2013-01-01
This study is aimed at setting a method of experimental parameter estimation for large-deforming nonlinear viscoelastic continuous fibre-reinforced composite material model. Specifically, arterial tissue was investigated during experimental research and parameter estimation studies, due to medical, scientific and socio-economic importance of soft tissue research. Using analytical formulations for specimens under combined inflation/extension/torsion on thick-walled cylindrical tubes, in vitro experiments were carried out with fresh sheep arterial segments, and parameter estimation procedures were carried out on experimental data. Model restrictions were pointed out using outcomes from parameter estimation. Needs for further studies that can be developed are discussed.
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik;
1995-01-01
and the growth of the biomass are described by the Monod model consisting of two nonlinear coupled first-order differential equations. The objective of this study was to estimate the kinetic parameters in the Monod model and to test whether the parameters from the three identical experiments have the same values....... Estimation of the parameters was obtained using an iterative maximum likelihood method and the test used was an approximative likelihood ratio test. The test showed that the three sets of parameters were identical only on a 4% alpha level....
THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS
Institute of Scientific and Technical Information of China (English)
赵晓东
1998-01-01
The non-linear inversion of rock mechanics parameters based on genetic algorithm ispresented. The principle and step of genetic algorithm is also given. A brief discussion of thismethod and an application example is presented at the end of this paper. From the satisfied re-sult, quick, convenient and practical new approach is developed to solve this kind of problems.
A Nonlinear Observer for Estimating Transverse Stability Parameters of Marine Surface Vessels
DEFF Research Database (Denmark)
Galeazzi, Roberto; Perez, Tristan
2011-01-01
This paper presents a nonlinear observer for estimating parameters associated with the restoring term of a roll motion model of a marine vessel in longitudinal waves. Changes in restoring, also referred to as transverse stability, can be the result of changes in the vessel’s centre of gravity due...
Positive Solutions for Nonlinear Singular Differential Systems Involving Parameter on the Half-Line
Directory of Open Access Journals (Sweden)
Lishan Liu
2012-01-01
Full Text Available By using the upper-lower solutions method and the fixed-point theorem on cone in a special space, we study the singular boundary value problem for systems of nonlinear second-order differential equations involving two parameters on the half-line. Some results for the existence, nonexistence and multiplicity of positive solutions for the problem are obtained.
Computation of the acoustic nonlinearity parameter in organic liquid binary mixtures
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Based on Jacobson's molecular free length theory in liquids and the relationship between the ultrasonic velocity and the molecular free length in organic liquids,the equation of the acoustic nonlinearity parameter in organic liquid binary mixtures is derived.The calculated values from the equation are in good agreement both with those from Apfel's and from Sehgal's mixture laws.
Directory of Open Access Journals (Sweden)
Milovanović Branislav
2007-01-01
Full Text Available Introduction: There are different proofs about association of autonomic nervous system dysfunction, especially nonlinear parameters, with higher mortality after myocardial infarction. Objective The objective of the study was to determine predictive value of Poincare plot as nonlinear parameter and other significant standard risk predictors: ejection fraction of the left ventricle, late potentials, ventricular arrhythmias, and QT interval. Method The study included 1081 patients with mean follow up of 28 months (ranging fom 0-80 months. End-point of the study was cardiovascular mortality. The following diagnostic methods were used during the second week: ECG with commercial software Schiller AT-10: short time spectral analysis of RR variability with analysis of Poincare plot as nonlinear parameter and late potentials; 24-hour ambulatory ECG monitoring: QT interval, RR interval, QT/RR slope, ventricular arrhythmias (Lown >II; echocardiography examinations: systolic disorder (defined as EF<40 %. Results There were 103 (9.52% cardiovascular deaths during the follow-up. In univariate analysis, the following parameters were significantly correlated with mortality: mean RR interval < 800 ms, QT and RR interval space relationship as mean RR interval < 800 ms and QT interval > 350 ms, positive late potentials, systolic dysfunction, Poincare plot as a point, ventricular arrhythmias (Lown > II. In multivariate analysis, the significant risk predictors were: Poincare plot as a point and mean RR interval lower than 800 ms. Conclusion Mean RR interval lower than 800 ms and nonlinear and space presentation of RR interval as a point Poincare plot were multivariate risk predictors.
SINGULARLY PERTURBED SOLUTION FOR THIRD ORDER NONLINEAR EQUATIONS WITH TWO PARAMETERS
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A class of singularly perturbed boundary value problems for nonlinear equation of the third order with two parameters is considered. Under suitable conditions, using the theory of differential inequalities the existence and asymptotic behavior of the solution for boundary value problem are studied.
Impact Parameter Dependent Parton Distributions for a Composite Relativistic System
Chakraborty, D
2004-01-01
We investigate the impact parameter dependent parton distributions for a relativistic composite system in light-front framework. We take an effective two-body spin-1/2 state, namely an electron dressed with a photon in QED. We express the impact parameter dependent parton distributions in terms of overlaps of light-cone wave functions. We obtain the scale dependence of both fermion and gauge boson distributions and show the distortion of the pdfs in the transverse space for transverse polarization of the state at one loop level.
Sensitivity analysis of distributed parameter elements In high-speed circuit networks
Institute of Scientific and Technical Information of China (English)
Lei DOU; Zhiquan WANG
2007-01-01
This paper presents an analysis method,based on MacCormack's technique,for the evaluation of the time domain sensitivity of distributed parameter elements in high-speed circuit networks.Sensitivities can be calculated from electrical and physical parameters of the distributed parameter elements.The proposed method is a direct numerical method of time-space discretization and does not require complicated mathematical deductive process.Therefore,it is very convenient to program this method.It can be applied to sensitivity analysis of general transmission lines in linear or nonlinear circuit networks.The proposed method is second-order-accurate.Numerical experiment is presented to demonstrate its accuracy and efficiency.
Extreme value distribution and reliability of nonlinear stochastic structures
Institute of Scientific and Technical Information of China (English)
Chen Jianbing; Li Jie
2005-01-01
A new approach to evaluate the extreme value distribution (EVD) of the response and reliability of general multi-DOF nonlinear stochastic structures is proposed. The approach is based on the recently developed probability density evolution method, which enables the instantaneous probability density functions of the stochastic responses to be captured.In the proposed method, a virtual stochastic process is first constructed to satisfy the condition that the extreme value of the response equals the value of the constructed process at a certain instant of time. The probability density evolution method is then applied to evaluate the instantaneous probability density function of the response, yielding the EVD. The reliability is therefore available through a simple integration over the safe domain. A numerical algorithm is developed using the Number Theoretical Method to select the discretized representative points. Further, a hyper-ball is imposed to sieve the points from the preceding point set in the hypercube. In the numerical examples, the EVD of random variables is evaluated and compared with the analytical solution. A frame structure is analyzed to capture the EVD of the response and the dynamic reliability. The investigations indicate that the proposed approach provides reasonable accuracy and efficiency.
A TRUST REGION METHOD FOR SOLVING DISTRIBUTED PARAMETER IDENTIFICATION PROBLEMS
Institute of Scientific and Technical Information of China (English)
Yan-fei Wang; Ya-xiang Yuan
2003-01-01
This paper is concerned with the ill-posed problems of identifying a parameter in an elliptic equation which appears in many applications in science and industry. Its solution is obtained by applying trust region method to a nonlinear least squares error problem.Trust region method has long been a popular method for well-posed problems. This paper indicates that it is also suitable for ill-posed problems. Numerical experiment is given to compare the trust region method with the Tikhonov regularization method. It seems that the trust region method is more promising.
Quantum stability of nonlinear wave type solutions with intrinsic mass parameter in QCD
Kim, Youngman; Lee, Bum-Hoon; Pak, D. G.; Park, Chanyong; Tsukioka, Takuya
2017-09-01
The problem of the existence of a stable vacuum field in pure QCD is revised. Our approach is based on using classical stationary nonlinear wave type solutions with an intrinsic mass scale parameter. Such solutions can be treated as quantum-mechanical wave functions describing massive spinless states in quantum theory. We verify whether nonlinear wave type solutions can form a stable vacuum field background within the framework of the effective action formalism. We demonstrate that there is a special class of stationary generalized Wu-Yang monopole solutions that are stable against quantum gluon fluctuations.
Directory of Open Access Journals (Sweden)
Jianping Cai
2003-01-01
Full Text Available A method of approximate potential is presented for the study of certain kinds of strongly nonlinear oscillators. This method is to express the potential for an oscillatory system by a polynomial of degree four such that the leading approximation may be derived in terms of elliptic functions. The advantage of present method is that it is valid for relatively large oscillations. As an application, the elapsed time of periodic motion of a strongly nonlinear oscillator with slowly varying parameters is studied in detail. Comparisons are made with other methods to assess the accuracy of the present method.
Two-parameters quasi-filled function algorithm for nonlinear integer programming
Institute of Scientific and Technical Information of China (English)
WANG Wei-xiang; SHANG You-lin; ZHANG Lian-sheng
2006-01-01
A quasi-filled function for nonlinear integer programming problem is given in this paper. This function contains two parameters which are easily to be chosen. Theoretical properties of the proposed quasi-filled function are investigated. Moreover,we also propose a new solution algorithm using this quasi-filled function to solve nonlinear integer programming problem in this paper. The examples with 2 to 6 variables are tested and computational results indicated the efficiency and reliability of the proposed quasi-filled function algorithm.
Non-Linear EMG Parameters for Differential and Early Diagnostics of Parkinson's Disease.
Meigal, Alexander Y; Rissanen, Saara M; Tarvainen, Mika P; Airaksinen, Olavi; Kankaanpää, Markku; Karjalainen, Pasi A
2013-01-01
The pre-clinical diagnostics is essential for management of Parkinson's disease (PD). Although PD has been studied intensively in the last decades, the pre-clinical indicators of that motor disorder have yet to be established. Several approaches were proposed but the definitive method is still lacking. Here we report on the non-linear characteristics of surface electromyogram (sEMG) and tremor acceleration as a possible diagnostic tool, and, in prospective, as a predictor for PD. Following this approach we calculated such non-linear parameters of sEMG and accelerometer signal as correlation dimension, entropy, and determinism. We found that the non-linear parameters allowed discriminating some 85% of healthy controls from PD patients. Thus, this approach offers considerable potential for developing sEMG-based method for pre-clinical diagnostics of PD. However, non-linear parameters proved to be more reliable for the shaking form of PD, while diagnostics of the rigid form of PD using EMG remains an open question.
Nonlinear EMG parameters for differential and early diagnostics of Parkinson's disease
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Alexander eMeigal
2013-09-01
Full Text Available The pre-clinical diagnostics is essential for management of Parkinson’s disease (PD. . Although PD has been studied intensively in the last decades, the pre-clinical indicators of that motor disorder have yet to be established. Several approaches were proposed but the definitive method is still lacking. Here we report on the non-linear characteristics of surface electromyogram (sEMG and tremor acceleration as a possible diagnostic tool, and, in prospective, as a predictor for PD. Following this approach we calculated such nonlinear parameters of sEMG and accelerometer signal as correlation dimension, entropy and determinism. We found that the nonlinear parameters allowed discriminating some 85% of healthy controls from PD patients. Thus, this approach offers considerable potential for developing sEMG-based method for pre-clinical diagnostics of PD. However, non-linear parameters proved to be more reliable for the shaking form of PD, while diagnostics of the rigid form of PD using EMG remains an open question.
Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes
Institute of Scientific and Technical Information of China (English)
许锋; 汪晔晔; 罗雄麟
2013-01-01
Chemical processes are usually nonlinear singular systems. In this study, a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes, which are augmented as state variables. Based on the observability of the singular system, this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters. When the observability is satisfied, the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer. The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation. With the catalyst circulation rate as the only unknown input without model error, one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst cir-culation rate. However, when uncertain model parameters also exist, additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.
Institute of Scientific and Technical Information of China (English)
Mohammad Pourmahmood Aghababa; Hassan Feizi
2012-01-01
This paper deals with the design of a novel nonsingular terminal sliding mode controller for finite-time synchronization of two different chaotic systems with fully unknown parameters and nonlinear inputs.We propose a novel nonsingular terminal sliding surface and prove its finite-time convergence to zero.We assume that both the master's and the slave's system parameters are unknown in advance.Proper adaptation laws are derived to tackle the unknown parameters.An adaptive sliding mode control law is designed to ensure the existence of the sliding mode in finite time.We prove that both reaching and sliding mode phases are stable in finite time.An estimation of convergence time is given.Two illustrative examples show the effectiveness and usefulness of the proposed technique.It is worthwhile noticing that the introduced nonsingular terminal sliding mode can be applied to a wide variety of nonlinear control problems.
Nemeth, Michael P.
2010-01-01
A comprehensive development of nondimensional parameters and equations for nonlinear and bifurcations analyses of quasi-shallow shells, based on the Donnell-Mushtari-Vlasov theory for thin anisotropic shells, is presented. A complete set of field equations for geometrically imperfect shells is presented in terms general of lines-of-curvature coordinates. A systematic nondimensionalization of these equations is developed, several new nondimensional parameters are defined, and a comprehensive stress-function formulation is presented that includes variational principles for equilibrium and compatibility. Bifurcation analysis is applied to the nondimensional nonlinear field equations and a comprehensive set of bifurcation equations are presented. An extensive collection of tables and figures are presented that show the effects of lamina material properties and stacking sequence on the nondimensional parameters.
A method to estimate the absolute ultrasonic nonlinearity parameter from relative measurements.
Kim, Jongbeom; Song, Dong-Gi; Jhang, Kyung-Young
2017-02-17
The ultrasonic nonlinearity parameter, β, is determined from the displacement amplitude of the second-order harmonic frequency component generated during the propagation of ultrasonic waves through a material. This parameter is generally referred to as the absolute parameter. Meanwhile, it is difficult to measure the small displacement amplitude of the second-order harmonic component; therefore, most studies measure the relative parameter determined from the detected signal amplitude. However, for quantitative assessment of material degradation, the absolute parameter is still required. This study proposes a method to estimate the absolute parameter for damaged material by measuring the relative parameter. This method is based on the fact that the fractional ratio of the relative parameters between different materials is identical to that of the absolute parameters after compensation for material dependent differences such as the wavenumber and detection-sensitivity. In order to experimentally verify the method, the relative parameters of heat-treated Al6061-T6 alloy specimens with different aging times were measured to compare with absolute parameters directly measured by piezo-electric detection. The results show that the fluctuations of both parameters with respect to aging time were very similar to each other, and that the absolute parameters estimated by the proposed method were in good agreement with those measured directly.
Recursive identification and tracking of parameters for linear and nonlinear multivariable systems
Sidar, M.
1975-01-01
The problem of identifying constant and variable parameters in multi-input, multi-output, linear and nonlinear systems is considered, using the maximum likelihood approach. An iterative algorithm, leading to recursive identification and tracking of the unknown parameters and the noise covariance matrix, is developed. Agile tracking, and accurate and unbiased identified parameters are obtained. Necessary conditions for a globally, asymptotically stable identification process are provided; the conditions proved to be useful and efficient. Among different cases studied, the stability derivatives of an aircraft were identified and some of the results are shown as examples.
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Houda Salhi
2016-01-01
Full Text Available This paper deals with the parameter estimation problem for multivariable nonlinear systems described by MIMO state-space Wiener models. Recursive parameters and state estimation algorithms are presented using the least squares technique, the adjustable model, and the Kalman filter theory. The basic idea is to estimate jointly the parameters, the state vector, and the internal variables of MIMO Wiener models based on a specific decomposition technique to extract the internal vector and avoid problems related to invertibility assumption. The effectiveness of the proposed algorithms is shown by an illustrative simulation example.
Stabilization of stochastic Hopfield neural network with distributed parameters
Institute of Scientific and Technical Information of China (English)
LUO Qi; DENG Feiqi; BAO Jundong; ZHAO Birong; FU Yuli
2004-01-01
In this paper, the stability of stochastic Hopfield neural network with distributed parameters is studied. To discuss the stability of systems, the main idea is to integrate the solution to systems in the space variable. Then, the integration is considered as the solution process of corresponding neural networks described by stochastic ordinary differential equations. A Lyapunov function is constructed and It(o) formula is employed to compute the derivative of the mean Lyapunov function along the systems, with respect to the space variable. It is difficult to treat stochastic systems with distributed parameters since there is no corresponding It(o) formula for this kind of system. Our method can overcome this difficulty. Till now, the research of stability and stabilization of stochastic neural networks with distributed parameters has not been considered.
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Yu-Chi Wang
2015-01-01
Full Text Available This paper presents a unified approach to nonlinear dynamic inversion control algorithm with the parameters for desired dynamics determined by using an eigenvalue assignment method, which may be applied in a very straightforward and convenient way. By using this method, it is not necessary to transform the nonlinear equations into linear equations by feedback linearization before beginning control designs. The applications of this method are not limited to affine nonlinear control systems or limited to minimum phase problems if the eigenvalues of error dynamics are carefully assigned so that the desired dynamics is stable. The control design by using this method is shown to be robust to modeling uncertainties. To validate the theory, the design of a UAV control system is presented as an example. Numerical simulations show the performance of the design to be quite remarkable.
Identification for a class of distributed parameter systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper discusses the identification for the distributed parameter system of gas reservoirs with het erogeneous carbonate matrices. Based on the relationship between the ad hoc function and the geological feature, we set up a general model with double porous structure, clarify its effect and significance, and present a series of results of sys tem identifications, including the basic content and methods, stabilizing functional and parameter identifiability etc. Us ing the perturbation of spectra of self-adjoint operators, the identifiability of the porosities and the ad hoc coefficient is demonstrated for the general structure model. This project indicates that the identification of a distributed parameter sys tem involves parameters, boundary position and structure.
Vismara, S. O.; Ricci, S.; Bellini, M.; Trittoni, L.
2016-06-01
The objective of the present paper is to describe a procedure to identify and model the non-linear behaviour of structural elements. The procedure herein applied can be divided into two main steps: the system identification and the finite element model updating. The application of the restoring force surface method as a strategy to characterize and identify localized non-linearities has been investigated. This method, which works in the time domain, has been chosen because it has `built-in' characterization capabilities, it allows a direct non-parametric identification of non-linear single-degree-of-freedom systems and it can easily deal with sine-sweep excitations. Two different application examples are reported. At first, a numerical test case has been carried out to investigate the modelling techniques in the case of non-linear behaviour based on the presence of a free-play in the model. The second example concerns the flap of the Intermediate eXperimental Vehicle that successfully completed its 100-min mission on 11 February 2015. The flap was developed under the responsibility of Thales Alenia Space Italia, the prime contractor, which provided the experimental data needed to accomplish the investigation. The procedure here presented has been applied to the results of modal testing performed on the article. Once the non-linear parameters were identified, they were used to update the finite element model in order to prove its capability of predicting the flap behaviour for different load levels.
Fully Nonlinear Boussinesq-Type Equations with Optimized Parameters for Water Wave Propagation
Institute of Scientific and Technical Information of China (English)
荆海晓; 刘长根; 龙文; 陶建华
2015-01-01
For simulating water wave propagation in coastal areas, various Boussinesq-type equations with improved properties in intermediate or deep water have been presented in the past several decades. How to choose proper Boussinesq-type equations has been a practical problem for engineers. In this paper, approaches of improving the characteristics of the equations, i.e. linear dispersion, shoaling gradient and nonlinearity, are reviewed and the advantages and disadvantages of several different Boussinesq-type equations are compared for the applications of these Boussinesq-type equations in coastal engineering with relatively large sea areas. Then for improving the properties of Boussinesq-type equations, a new set of fully nonlinear Boussinseq-type equations with modified representative velocity are derived, which can be used for better linear dispersion and nonlinearity. Based on the method of minimizing the overall error in different ranges of applications, sets of parameters are determined with optimized linear dispersion, linear shoaling and nonlinearity, respectively. Finally, a test example is given for validating the results of this study. Both results show that the equations with optimized parameters display better characteristics than the ones obtained by matching with padé approximation.
Fully nonlinear Boussinesq-type equations with optimized parameters for water wave propagation
Jing, Hai-xiao; Liu, Chang-gen; Long, Wen; Tao, Jian-hua
2015-06-01
For simulating water wave propagation in coastal areas, various Boussinesq-type equations with improved properties in intermediate or deep water have been presented in the past several decades. How to choose proper Boussinesq-type equations has been a practical problem for engineers. In this paper, approaches of improving the characteristics of the equations, i.e. linear dispersion, shoaling gradient and nonlinearity, are reviewed and the advantages and disadvantages of several different Boussinesq-type equations are compared for the applications of these Boussinesq-type equations in coastal engineering with relatively large sea areas. Then for improving the properties of Boussinesq-type equations, a new set of fully nonlinear Boussinseq-type equations with modified representative velocity are derived, which can be used for better linear dispersion and nonlinearity. Based on the method of minimizing the overall error in different ranges of applications, sets of parameters are determined with optimized linear dispersion, linear shoaling and nonlinearity, respectively. Finally, a test example is given for validating the results of this study. Both results show that the equations with optimized parameters display better characteristics than the ones obtained by matching with padé approximation.
Shenoy, V. B.; Potyondy, D. O.; Atluri, S. N.
1994-09-01
A computational methodology for obtaining nonlinear fracture parameters which account for the effects of plasticity at the tips of a bulging crack in a pressurised aircraft fuselage is developed. The methodology involves a hierarchical three stage analysis (global, intermediate, and local) of the cracked fuselage, with the crack incorporated into the model at each stage. The global analysis is performed using a linear elastic shell finite element model in which the stiffeners are treated as beam elements. The geometrically nonlinear nature of the bulging phenomenon is emulated in the intermediate analysis using a geometrically nonlinear shell finite element model. The local analysis is a three-dimensional solid finite element model of the cracked skin using a hypoelastic-plastic rate formulation. Kinematic boundary conditions for each stage are obtained from the preceding stage in the hierarchy using a general mesh independent mechanism. The T *integral, which accounts for both large deformations and plasticity, is taken to be the fracture parameter characterising the severity of the conditions at the crack tip, and is evaluated from the local analysis using the Equivalent Domain Integral (EDI) method. The implementation of the EDI technique for finite deformations in shell space is also outlined. The methodology is applied to a number of example problems for which correction factors relating the nonlinear T * values to those obtained from a linear elastic stiffened shell analysis are computed. The issue of flapping is addressed by investigating the behaviour of the longitudinal stress parallel to the crack for various cases.
Directory of Open Access Journals (Sweden)
U. Filobello-Nino
2015-01-01
Full Text Available We propose an approximate solution of T-F equation, obtained by using the nonlinearities distribution homotopy perturbation method (NDHPM. Besides, we show a table of comparison, between this proposed approximate solution and a numerical of T-F, by establishing the accuracy of the results.
Energy Technology Data Exchange (ETDEWEB)
Ma Huanfei [Center for Computational Systems Biology, Fudan University, Shanghai 200433 (China)] [School of Computer Science, Fudan University, Shanghai 200433 (China); Lin Wei, E-mail: wlin@fudan.edu.c [Center for Computational Systems Biology, Fudan University, Shanghai 200433 (China)] [School of Mathematical Sciences, Fudan University, Shanghai 200433 (China)] [Key Laboratory of Mathematics for Nonlinear Sciences (Fudan University), Ministry of Education (China)] [CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai 200031 (China)
2009-12-28
The existing adaptive synchronization technique based on the stability theory and invariance principle of dynamical systems, though theoretically proved to be valid for parameters identification in specific models, is always showing slow convergence rate and even failed in practice when the number of parameters becomes large. Here, for parameters update, a novel nonlinear adaptive rule is proposed to accelerate the rate. Its feasibility is validated by analytical arguments as well as by specific parameters identification in the Lotka-Volterra model with multiple species. Two adjustable factors in this rule influence the identification accuracy, which means that a proper choice of these factors leads to an optimal performance of this rule. In addition, a feasible method for avoiding the occurrence of the approximate linear dependence among terms with parameters on the synchronized manifold is also proposed.
1979-01-01
A nonlinear, maximum likelihood, parameter identification computer program (NLSCIDNT) is described which evaluates rotorcraft stability and control coefficients from flight test data. The optimal estimates of the parameters (stability and control coefficients) are determined (identified) by minimizing the negative log likelihood cost function. The minimization technique is the Levenberg-Marquardt method, which behaves like the steepest descent method when it is far from the minimum and behaves like the modified Newton-Raphson method when it is nearer the minimum. Twenty-one states and 40 measurement variables are modeled, and any subset may be selected. States which are not integrated may be fixed at an input value, or time history data may be substituted for the state in the equations of motion. Any aerodynamic coefficient may be expressed as a nonlinear polynomial function of selected 'expansion variables'.
Two-parameter non-linear spacetime perturbations gauge transformations and gauge invariance
Bruni, M; Sopuerta, C F; Bruni, Marco; Gualtieri, Leonardo; Sopuerta, Carlos F.
2003-01-01
An implicit fundamental assumption in relativistic perturbation theory is that there exists a parametric family of spacetimes that can be Taylor expanded around a background. The choice of the latter is crucial to obtain a manageable theory, so that it is sometime convenient to construct a perturbative formalism based on two (or more) parameters. The study of perturbations of rotating stars is a good example: in this case one can treat the stationary axisymmetric star using a slow rotation approximation (expansion in the angular velocity Omega), so that the background is spherical. Generic perturbations of the rotating star (say parametrized by lambda) are then built on top of the axisymmetric perturbations in Omega. Clearly, any interesting physics requires non-linear perturbations, as at least terms lambda Omega need to be considered. In this paper we analyse the gauge dependence of non-linear perturbations depending on two parameters, derive explicit higher order gauge transformation rules, and define gaug...
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Y. Orlov
2002-01-01
Full Text Available The paper is intended to be of tutorial value for Schwartz' distributions theory in nonlinear setting. Mathematical models are presented for nonlinear systems which admit both standard and impulsive inputs. These models are governed by differential equations in distributions whose meaning is generalized to involve nonlinear, non single-valued operating over distributions. The set of generalized solutions of these differential equations is defined via closure, in a certain topology, of the set of the conventional solutions corresponding to standard integrable inputs. The theory is exemplified by mechanical systems with impulsive phenomena, optimal impulsive feedback synthesis, sampled-data filtering of stochastic and deterministic dynamic systems.
Ndoye, Ibrahima
2014-12-01
In this paper, an adaptive observer design with parameter identification for a nonlinear system with external perturbations and unknown parameters is proposed. The states of the nonlinear system are estimated by a nonlinear observer and the unknown parameters are also adapted to their values. Sufficient conditions for the stability of the adaptive observer error dynamics are derived in terms of linear matrix inequalities. Simulation results for chaotic Lorenz systems with unknown parameters in the presence of external perturbations are given to illustrate the effectiveness of our proposed approach. © 2014 IEEE.
Small x nonlinear evolution with impact parameter and the structure function data
Berger, Jeffrey
2011-01-01
Nonlinear evolution at small values of Bjorken x is evaluated numerically using the dipole framework with impact parameter dependence. Confinement effects are modeled by including masses into the evolution. Sensitivity of the predictions due to different prescriptions of the cuts on large dipole sizes is investigated. Running coupling effects are taken into account in this analysis. Finally, a comparison with the inclusive data from HERA on the structure functions F2 and FL is performed.
Positive solutions for a nonlinear periodic boundary-value problem with a parameter
Directory of Open Access Journals (Sweden)
Jingliang Qiu
2012-08-01
Full Text Available Using topological degree theory with a partially ordered structure of space, sufficient conditions for the existence and multiplicity of positive solutions for a second-order nonlinear periodic boundary-value problem are established. Inspired by ideas in Guo and Lakshmikantham [6], we study the dependence of positive periodic solutions as a parameter approaches infinity, $$ lim_{lambdao +infty}|x_{lambda}|=+infty,quadhbox{or}quad lim_{lambdao+infty}|x_{lambda}|=0. $$
Uniform Approximate Estimation for Nonlinear Nonhomogenous Stochastic System with Unknown Parameter
2012-01-01
The error bound in probability between the approximate maximum likelihood estimator (AMLE) and the continuous maximum likelihood estimator (MLE) is investigated for nonlinear nonhomogenous stochastic system with unknown parameter. The rates of convergence of the approximations for Itô and ordinary integral are introduced under some regular assumptions. Based on these results, the in probability rate of convergence of the approximate log-likelihood function to the true continuous log-likelihoo...
Chandrasekhar equations and computational algorithms for distributed parameter systems
Burns, J. A.; Ito, K.; Powers, R. K.
1984-01-01
The Chandrasekhar equations arising in optimal control problems for linear distributed parameter systems are considered. The equations are derived via approximation theory. This approach is used to obtain existence, uniqueness, and strong differentiability of the solutions and provides the basis for a convergent computation scheme for approximating feedback gain operators. A numerical example is presented to illustrate these ideas.
Distributed parameter modelling of flexible spacecraft: Where's the beef?
Hyland, D. C.
1994-01-01
This presentation discusses various misgivings concerning the directions and productivity of Distributed Parameter System (DPS) theory as applied to spacecraft vibration control. We try to show the need for greater cross-fertilization between DPS theorists and spacecraft control designers. We recommend a shift in research directions toward exploration of asymptotic frequency response characteristics of critical importance to control designers.
Port controlled Hamiltonian representation of distributed parameter systems
Maschke, B.M.; van der Schaft, Arjan
2000-01-01
A port controlled Hamiltonian formulation of the dynamics of distributed parameter systems is presented, which incorporates the energy flow through the boundary of the domain of the system, and which allows to represent the system as a boundary control Hamiltonian system. This port controlled
Directory of Open Access Journals (Sweden)
KOTSUR, M.
2015-05-01
Full Text Available We give a solution of optimal control problem for distributed parameter systems described by nonlinear partial differential equations with nonstandard boundary conditions. The variational method is used to obtain the general form of the necessary conditions of optimality. A suitable algorithm based on the numerical method of successive approximations has been constructed for computing the optimal control functions. The results are applied for optimization of transient thermoelectric cooling process. Optimal dependences of current on time have been calculated for thermoelectric cooler power supply with the purpose of minimizing the cooling temperature within a preset time interval.
Cao, Jiguo
2012-01-01
Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.
Improved Accuracy of Nonlinear Parameter Estimation with LAV and Interval Arithmetic Methods
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Humberto Muñoz
2009-06-01
Full Text Available The reliable solution of nonlinear parameter es- timation problems is an important computational problem in many areas of science and engineering, including such applications as real time optimization. Its goal is to estimate accurate model parameters that provide the best ﬁt to measured data, despite small- scale noise in the data or occasional large-scale mea- surement errors (outliers. In general, the estimation techniques are based on some kind of least squares or maximum likelihood criterion, and these require the solution of a nonlinear and non-convex optimiza- tion problem. Classical solution methods for these problems are local methods, and may not be reliable for ﬁnding the global optimum, with no guarantee the best model parameters have been found. Interval arithmetic can be used to compute completely and reliably the global optimum for the nonlinear para- meter estimation problem. Finally, experimental re- sults will compare the least squares, l2, and the least absolute value, l1, estimates using interval arithmetic in a chemical engineering application.
Study of Linear and Non-Linear Optical Parameters of Zinc Selenide Thin Film
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H. N. Desai
2015-06-01
Full Text Available Thin film of Zinc Selenide (ZnSe was deposited onto transparent glass substrate by thermal evaporation technique. ZnSe thin film was characterized by UV-Visible spectrophotometer within the wavelength range of 310 nm-1080 nm. The Linear optical parameters (linear optical absorption, extinction coefficient, refractive index and complex dielectric constant of ZnSe thin film were analyzed from absorption spectra. The optical band gap and Urbach energy were obtained by Tauc’s equation. The volume and surface energy loss function of ZnSe thin film were obtained by complex dielectric constant. The Dispersion parameters (dispersion energy, oscillation energy, moment of optical dispersion spectra, static dielectric constant and static refractive index were calculated using theoretical Wemple-DiDomenico model. The oscillation strength, oscillator wavelength, high frequency dielectric constant and high frequency refractive index were calculated by single Sellmeier oscillator model. Also, Lattice dielectric constant, N/m* and plasma resonance frequency were obtained. The electronic polarizibility of ZnSe thin film was estimated by Clausius-Mossotti local field polarizibility. The nonlinear optical parameters (non-linear susceptibility and non-linear refractive index were estimated.
Cao, Jiguo; Huang, Jianhua Z; Wu, Hulin
2012-01-01
Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.
Control synthesis of linear distributed parameter switched systems
Institute of Scientific and Technical Information of China (English)
Leping Bao; Shumin Fei; Lin Chai
2015-01-01
The control synthesis for switched systems is extended to distributed parameter switched systems in Hilbert space. Based on semigroup and operator theory, by means of multiple Lyapunov method incorporated average dwel time approach, sufficient con-ditions are derived in terms of linear operator inequalities frame-work for distributed parameter switched systems. Being applied to one dimensional heat propagation switched systems, these lin-ear operator inequalities are reduced to linear matrix inequalities subsequently. In particular, the state feedback gain matrices and the switching law are designed, and the state decay estimate is explicitly given whose decay coefficient completely depends on the system’s parameter and the boundary condition. Final y, two numerical examples are given to il ustrate the proposed method.
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation
2016-08-29
In this note, the joint state and parameters estimation problem for nonlinear multi-input multi-output descriptor systems is considered. Asymptotic convergence of the adaptive descriptor observer is established by a sufficient set of linear matrix inequalities for the noise-free systems. The noise corrupted systems are also considered and it is shown that the state and parameters estimation errors are bounded for bounded noises. In addition, if the noises are bounded and have zero mean, then the estimation errors asymptotically converge to zero in the mean. The performance of the proposed adaptive observer is illustrated by a numerical example.
Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must be constructed based on the data. This paper proposes the Particle Swarm Optimization (PSO) algorithm for estimating the parameters such a curve. The PSO algorithm is an evolutional method based on a very simple concept. Comparison of PSO results with those of GA and LS methods showed that the PSO algorithm is more effective for estimating the parameters of the above curve.
Gallot, T.; Fehler, M. C.; Brown, S. R.; Buns, D.; Szabo, T.; Malcolm, A. E.
2013-12-01
The nonlinear mechanical behavior of rocks is a well known phenomenon at a laboratory scale and has been observed during earthquakes, slow slip events, volcanic activity, reservoir fracturing, etc. he present work explores the possibility of measuring nonlinear parameters in a semi-infinite medium. Contrary to existing methods that rely on vibrating a sample at a fixed resonant frequency, a pulsed wave is used to create a high amplitude perturbation (the pump) responsible for the nonlinear response. At the same time, a low amplitude wave probes the material to measure changes in elastic properties. Laboratory experiments have been performed in rocks (berea sandstones) to explore the possibility of using such a method for Earth imaging. The strain created by the pump (a shear wave in the tens of kHz), is on the order of a microstrain and is measured by laser vibrometry and extrapolated to the whole sample by a finite difference simulation. A compressional pulse (in the hundreds of kHz range) probes the 15-cm size sample. The variation in time of flight is related to a change in elasticity as described as a function of the strain through quadratic and cubic nonlinearities. Those nonlinear coefficients are shown to be sensitive to several environmental parameters such as temperature, humidity, and also physical properties such as the amplitude of the strain and the relative orientation of the pump and the probing wave. Experimental set-up: a P-wave transducer generates an ultrasonic pulse at 500 kHz recorded by an identical transducer after propagation through the sample. The medium is then perturbed with a S-wave transducer on the top of the sample at 50 kHz .
Weng, Su-Ming; Sheng, Zheng-Ming; Zhang, Jie
2009-11-01
Inverse bremsstrahlung (IB) absorption and evolution of the electron distribution function (EDF) in a wide laser intensity range (10;{12}-10;{17} W/cm;{2}) have been studied systematically by a two velocity-dimension Fokker-Planck code. It is found that Langdon's IB operator overestimates the absorption rate at high laser intensity, consequently with an overdistorted non-Maxwellian EDF. According to the small anisotropy of EDF in the oscillation frame, we introduce an IB operator which is similar to Langdon's but without the low laser intensity limit. This operator is appropriate for self-consistently tackling the nonlinear effects of high laser intensity as well as non-Maxwellian EDF. Particularly, our operator is capable of treating IB absorption properly in the indirect and direct-drive inertial confinement fusion schemes with the National Ignition Facility and Laser MegaJoule laser parameters at focused laser intensity beyond 10;{15} W/cm;{2} .
Directory of Open Access Journals (Sweden)
Arakelyan Sh. Kh.
2015-09-01
Full Text Available We suggest to apply the Bubnov–Galerkin procedure to solve scanning control problems for systems with distributed parameters. The algorithm is described in details for three-dimensional linear heat equation It allows to reduce the solution of control problem to finite-dimensional nonlinear moments problem. The procedure of derivation of moments problem is illustrated in details on the example of one-dimensional equation of thermal conductivity. The solution of obtained moments problem is found in a particular case. Based on obtained results a computer simulation is done using COMSOL Multiphysics platform in one-dimensional case for a rod. The main dependences of control function against input data of the problem are revealed. The state of the rod for several (constant values of the source intensity is expressed in terms of graphs and illustrations. Corresponding illustrations are brought in case of control absence (null-power source for comparison. An effective numerical scheme for solving the obtained system of nonlinear constraints is suggested in the case of extended class of admissible controls. Calculation of control parameters is reduced to the simplest problem of nonlinear programming.
Iterative methods for distributed parameter estimation in parabolic PDE
Energy Technology Data Exchange (ETDEWEB)
Vogel, C.R. [Montana State Univ., Bozeman, MT (United States); Wade, J.G. [Bowling Green State Univ., OH (United States)
1994-12-31
The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.
Nonlinear Inverse Problem for an Ion-Exchange Filter Model: Numerical Recovery of Parameters
Directory of Open Access Journals (Sweden)
Balgaisha Mukanova
2015-01-01
Full Text Available This paper considers the problem of identifying unknown parameters for a mathematical model of an ion-exchange filter via measurement at the outlet of the filter. The proposed mathematical model consists of a material balance equation, an equation describing the kinetics of ion-exchange for the nonequilibrium case, and an equation for the ion-exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion-exchange and several parameters. First, a numerical solution of the direct problem, the calculation of the impurities concentration at the outlet of the filter, is provided. Then, the inverse problem, finding the parameters of the ion-exchange process in nonequilibrium conditions, is formulated. A method for determining the approximate values of these parameters from the impurities concentration measured at the outlet of the filter is proposed.
Non-linear relationship between combustion kinetic parameters and coal quality
Institute of Scientific and Technical Information of China (English)
Jian-guo YANG; Xiao-long ZHANG; Hong ZHAO; Li SHEN
2012-01-01
Combustion kinetic parameters (i.e.,activation energy and frequency factor) of coal have been proven to relate closely to coal properties; however,the quantitative relationship between them still requires further study.This paper adopts a support vector regression machine (SVR) to generate the models of the non-linear relationship between combustion kinetic parameters and coal quality.Kinetic analyses on the thermo-gravimetry (TG) data of 80 coal samples were performed to prepare training data and testing data for the SVR.The models developed were used in the estimation of the combustion kinetic parameters of ten testing samples.The predicted results showed that the root mean square errors (RMSEs) were 2.571 for the activation energy and 0.565for the frequency factor in logarithmic form,respectively.TG curves defined by predicted kinetic parameters were fitted to the experimental data with a high degree of precision.
Distributed control design for nonlinear output agreement in convergent systems
Weitenberg, Erik; De Persis, Claudio
2015-01-01
This work studies the problem of output agreement in homogeneous networks of nonlinear dynamical systems under time-varying disturbances using controllers placed at the nodes of the networks. For the class of contractive systems, necessary and sufficient conditions for output agreement are derived,
Control of Groundwater Remediation Process as Distributed Parameter System
Directory of Open Access Journals (Sweden)
Mendel M.
2014-12-01
Full Text Available Pollution of groundwater requires the implementation of appropriate solutions which can be deployed for several years. The case of local groundwater contamination and its subsequent spread may result in contamination of drinking water sources or other disasters. This publication aims to design and demonstrate control of pumping wells for a model task of groundwater remediation. The task consists of appropriately spaced soil with input parameters, pumping wells and control system. Model of controlled system is made in the program MODFLOW using the finitedifference method as distributed parameter system. Control problem is solved by DPS Blockset for MATLAB & Simulink.
Nonlinear model predictive control using parameter varying BP-ARX combination model
Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.
2012-03-01
A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.
Institute of Scientific and Technical Information of China (English)
CHEN Xiongzi; YU Jinsong; TANG Diyin; WANG Yingxun
2012-01-01
Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis.However,for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters,the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models.To address the problem,this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis.This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances.Meantime,the propagation of prediction uncertainty is emphatically taken into account.Therefore,PF-LSSVR avoids over-dependency on system state models in prediction phase.With a two-sided failure definition,the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward.The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.
Davis, Craig Warren; Di Toro, Dominic M
2015-07-07
Procedures for accurately predicting linear partition coefficients onto various sorbents (e.g., organic carbon, soils, clay) are reliable and well established. However, similar procedures for the prediction of sorption parameters of nonlinear isotherm models are not. The purpose of this paper is to present a procedure for predicting nonlinear isotherm parameters, specifically the median Langmuir binding constants, K̃L, obtained utilizing the single-chemical parameter log-normal Langmuir isotherm developed in the accompanying work. A reduced poly parameter linear free energy relationship (pp-LFER) is able to predict median Langmuir binding constants for graphite, charcoal, and Darco granular activated carbon (GAC) adsorption data. For the larger F400 GAC data set, a single pp-LFER model was insufficient, as a plateau is observed for the median Langmuir binding constants of larger molecular volume sorbates. This volumetric cutoff occurs in proximity to the median pore diameter for F400 GAC. A log-linear relationship exists between the aqueous solubility of these large compounds and their median Langmuir binding constants. Using this relationship for the chemicals above the volumetric cutoff and the pp-LFER below the cutoff, the median Langmuir binding constants can be predicted with a root-mean square error for graphite (n = 13), charcoal (n = 11), Darco GAC (n = 14), and F400 GAC (n = 44) of 0.129, 0.307, 0.407, and 0.424, respectively.
Thermal Diffusivity Identification of Distributed Parameter Systems to Sea Ice
Directory of Open Access Journals (Sweden)
Liqiong Shi
2013-01-01
Full Text Available A method of optimal control is presented as a numerical tool for solving the sea ice heat transfer problem governed by a parabolic partial differential equation. Taken the deviation between the calculated ice temperature and the measurements as the performance criterion, an optimal control model of distributed parameter systems with specific constraints of thermal properties of sea ice was proposed to determine the thermal diffusivity of sea ice. Based on sea ice physical processes, the parameterization of the thermal diffusivity was derived through field data. The simulation results illustrated that the identified parameterization of the thermal diffusivity is reasonably effective in sea ice thermodynamics. The direct relation between the thermal diffusivity of sea ice and ice porosity is physically significant and can considerably reduce the computational errors. The successful application of this method also explained that the optimal control model of distributed parameter systems in conjunction with the engineering background has great potential in dealing with practical problems.
Escape time from potential wells of strongly nonlinear oscillators with slowly varying parameters
Directory of Open Access Journals (Sweden)
Cai Jianping
2005-01-01
Full Text Available The effect of negative damping to an oscillatory system is to force the amplitude to increase gradually and the motion will be out of the potential well of the oscillatory system eventually. In order to deduce the escape time from the potential well of quadratic or cubic nonlinear oscillator, the multiple scales method is firstly used to obtain the asymptotic solutions of strongly nonlinear oscillators with slowly varying parameters, and secondly the character of modulus of Jacobian elliptic function is applied to derive the equations governing the escape time. The approximate potential method, instead of Taylor series expansion, is used to approximate the potential of an oscillation system such that the asymptotic solution can be expressed in terms of Jacobian elliptic function. Numerical examples verify the efficiency of the present method.
Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm
Directory of Open Access Journals (Sweden)
V. Rajinikanth
2012-01-01
Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.
Impact Parameter Dependent Parton Distributions for a Relativistic Composite System
Chakraborty, D
2004-01-01
We investigate the impact parameter dependent parton distributions for a relativistic composite system in light-front framework. We express them in terms of overlaps of light-cone wave functions for a self consistent two-body spin-1/2 state, namely an electron dressed with a photon in QED. The pdfs are distorted in the transverse space for transverse polarization of the state at one loop level.
Devak, Manjula; Dhanya, Ct
2017-04-01
The scrupulous selection of critical spatial and temporal resolution and the evaluation of optimum values for various model parameters are essential aspects in any hydrological modelling study. The accurate assessment of various model parameters is vitally important for the detailed and complete representation of the various physical processes illustrating land-atmosphere interaction. Studies in the past have taken up various auto-calibration and parameter transferability schemes to address these; but the heterogeneity of calibration parameters across grids is greatly ignored often. In many studies, heterogeneity is often compromised through the usual interpolation approaches adopted across grids. In the present study, we focus to analyze the response of a catchment by adopting a heterogeneous and homogeneous parameter distribution in the hydrological model. The semi-distributed hydrological model, Variable Infiltration Capacity (VIC-3L) model, which offers sub-grid variability in soil moisture storage capacity and vegetation classes, is used for this comparison. Nine model parameters are selected for calibrating the VIC-3L model, namely variable infiltration curve parameter (infilt), maximum velocity of base flow for each grid cells (DSmax), fraction of DSmax where non-linear base flow begins (DS, fraction of maximum soil moisture where non-linear base flow occurs (WS), depth of 2nd soil layer (D2), depth of 3rd soil layer (D3), exponent used in baseflow curve (c), advection coefficient (C) and diffusion coefficient (D). Latin-Hypercube sampling is adopted to sample these nine parameters. In homogenous approach, the traditional way of constant soil parameter distribution (HoSCP) is adopted to prepare the parameter set. While, in heterogeneous approach, grid-to-grid variability is ensured by constructing a Heterogeneous Soil Calibration Parameter (HeSCP) set through systematic sampling of already sampled set. The sampling size is made equal to the number of grids
A new analytic method with a convergence-control parameter for solving nonlinear problems
Zhang, Xiaolong
2016-01-01
In this paper, a new analytic method with a convergence-control parameter $c$ is first proposed. The parameter $c$ is used to adjust and control the convergence region and rate of the resulting series solution. It turns out that the convergence region and rate can be greatly enlarged by choosing a proper value of $c$. Furthermore, a numerical approach for finding the optimal value of the convergence-control parameter is given. At the same time, it is found that the traditional Adomian decomposition method is only a special case of the new method. The effectiveness and applicability of the new technique are demonstrated by several physical models including nonlinear heat transfer problems, nano-electromechanical systems, diffusion and dissipation phenomena, and dispersive waves. Moreover, the ideas proposed in this paper may offer us possibilities to greatly improve current analytic and numerical techniques.
Identification of parameters in nonlinear geotechnical models using extenden Kalman filter
Directory of Open Access Journals (Sweden)
Nestorović Tamara
2014-01-01
Full Text Available Direct measurement of relevant system parameters often represents a problem due to different limitations. In geomechanics, measurement of geotechnical material constants which constitute a material model is usually a very diffcult task even with modern test equipment. Back-analysis has proved to be a more effcient and more economic method for identifying material constants because it needs measurement data such as settlements, pore pressures, etc., which are directly measurable, as inputs. Among many model parameter identification methods, the Kalman filter method has been applied very effectively in recent years. In this paper, the extended Kalman filter – local iteration procedure incorporated with finite element analysis (FEA software has been implemented. In order to prove the effciency of the method, parameter identification has been performed for a nonlinear geotechnical model.
Nonlinear functional response parameter estimation in a stochastic predator-prey model.
Gilioli, Gianni; Pasquali, Sara; Ruggeri, Fabrizio
2012-01-01
Parameter estimation for the functional response of predator-prey systems is a critical methodological problem in population ecology. In this paper we consider a stochastic predator-prey system with non-linear Ivlev functional response and propose a method for model parameter estimation based on time series of field data. We tackle the problem of parameter estimation using a Bayesian approach relying on a Markov Chain Monte Carlo algorithm. The efficiency of the method is tested on a set of simulated data. Then, the method is applied to a predator-prey system of importance for Integrated Pest Management and biological control, the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis. The model is estimated on a dataset obtained from a field survey. Finally, the estimated model is used to forecast predator-prey dynamics in similar fields, with slightly different initial conditions.
A Semismooth Newton Method for Nonlinear Parameter Identification Problems with Impulsive Noise
Clason, Christian
2012-01-01
This work is concerned with nonlinear parameter identification in partial differential equations subject to impulsive noise. To cope with the non-Gaussian nature of the noise, we consider a model with L 1 fitting. However, the nonsmoothness of the problem makes its efficient numerical solution challenging. By approximating this problem using a family of smoothed functionals, a semismooth Newton method becomes applicable. In particular, its superlinear convergence is proved under a second-order condition. The convergence of the solution to the approximating problem as the smoothing parameter goes to zero is shown. A strategy for adaptively selecting the regularization parameter based on a balancing principle is suggested. The efficiency of the method is illustrated on several benchmark inverse problems of recovering coefficients in elliptic differential equations, for which one- and two-dimensional numerical examples are presented. © by SIAM.
Flaw strength distributions and statistical parameters for ceramic fibers: The normal distribution
R'Mili, M.; Godin, N.; Lamon, J.
2012-05-01
The present paper investigates large sets of ceramic fibre failure strengths (500 to 1000 data) produced using tensile tests on tows that contained either 500 or 1000 filaments. The probability density function was determined through acoustic emission monitoring which allowed detection and counting of filament fractures. The statistical distribution of filament strengths was described using the normal distribution. The Weibull equation was then fitted to this normal distribution for estimation of statistical parameters. A perfect agreement between both distributions was obtained, and a quite negligible scatter in statistical parameters was observed, as opposed to the wide variability that is reported in the literature. Thus it was concluded that flaw strengths are distributed normally and that the statistical parameters that were derived are the true ones. In a second step, the conventional method of estimation of Weibull parameters was applied to these sets of data and, then, to subsets selected randomly. The influence of other factors involved in the conventional method of determination of statistical parameters is discussed. It is demonstrated that selection of specimens, sample size, and method of construction of so-called Weibull plots are responsible for statistical parameters variability.
Modulated Pulses Based High Spatial Resolution Distributed Fiber System for Multi-Parameter Sensing
Zhang, Jingdong; Zhou, Huan; Li, Yang; Liu, Min; Huang, Wei
2016-01-01
We demonstrate a hybrid distributed fiber sensing system for multi-parameter detection. The integration of phase-sensitive optical time domain reflectometry ({\\Phi}-OTDR) and Brillouin optical time domain reflectometry (B-OTDR) enables measurement of vibration, temperature and strain. Exploiting the fast changing property of vibration and the static property of temperature and strain, the laser pulse width and intensity are modulated and then injected into the single-mode sensing fiber proportionally, so that the three concerned parameters can be extracted simultaneously by only one photo-detector and data acquisition channel. Combining with advanced data processing methods, the modulation of laser pulse brings additional advantages because of trade and balance between the backscattering light power and nonlinear effect noise, which enhances the signal-to-noise ratio, and enables sub-meter level spatial resolution together with long sensing distance. The proposed method realizes up to 4.8 kHz vibration sensin...
A filter algorithm for multi-measurement nonlinear system with parameter perturbation
Institute of Scientific and Technical Information of China (English)
GUO Yun-fei; WEI Wei; XUE An-ke; MAO Dong-cai
2006-01-01
An improved interacting multiple models particle filter (IMM-PF) algorithm is proposed for multi-measurement nonlinear system with parameter perturbation. It divides the perturbation region into sub-regions and assigns each of them a particle filter. Hence the perturbation problem is converted into a multi-model filters problem. It combines the multiple measurements into a fusion value according to their likelihood function. In the simulation study, we compared it with the IMM-KF and the H-infinite filter; the results testify to its advantage over the other two methods.
DEFF Research Database (Denmark)
Kimiaeifar, Amin; Lund, Erik; Thomsen, Ole Thybo;
2010-01-01
In this work, an analytical method, which is referred to as Parameter-expansion Method is used to obtain the exact solution for the problem of nonlinear vibrations of an inextensible beam. It is shown that one term in the series expansion is sufficient to obtain a highly accurate solution, which ...... is valid for the whole domain of the problem. A comparison of the obtained the numerical solution demonstrates that PEM is effective and convenient for solving such problems. After validation of the obtained results, the system response and stability are also discussed....
Linear and nonlinear ARMA model parameter estimation using an artificial neural network
Chon, K. H.; Cohen, R. J.
1997-01-01
This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.
Abate, Alexandra; Teodoro, Luis F A; Warren, Michael S; Hendry, Martin
2008-01-01
We investigate methods to best estimate the normalisation of the mass density fluctuation power spectrum (sigma_8) using peculiar velocity data from a survey like the Six degree Field Galaxy Velocity Survey (6dFGSv). We focus on two potential problems (i) biases from nonlinear growth of structure and (ii) the large number of velocities in the survey. Simulations of LambdaCDM-like models are used to test the methods. We calculate the likelihood from a full covariance matrix of velocities averaged in grid cells. This simultaneously reduces the number of data points and smooths out nonlinearities which tend to dominate on small scales. We show how the averaging can be taken into account in the predictions in a practical way, and show the effect of the choice of cell size. We find that a cell size can be chosen that significantly reduces the nonlinearities without significantly increasing the error bars on cosmological parameters. We compare our results with those from a principal components analysis following Wa...
Albert, Carlo; Ulzega, Simone; Stoop, Ruedi
2016-04-01
Parameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find parameter values that are able to explain the observed data. In many situations, the dominant sources of uncertainty must be included into the model for making reliable predictions. This naturally leads to stochastic models. Stochastic models render parameter inference much harder, as the aim then is to find a distribution of likely parameter values. In Bayesian statistics, which is a consistent framework for data-driven learning, this so-called posterior distribution can be used to make probabilistic predictions. We propose a novel, exact, and very efficient approach for generating posterior parameter distributions for stochastic differential equation models calibrated to measured time series. The algorithm is inspired by reinterpreting the posterior distribution as a statistical mechanics partition function of an object akin to a polymer, where the measurements are mapped on heavier beads compared to those of the simulated data. To arrive at distribution samples, we employ a Hamiltonian Monte Carlo approach combined with a multiple time-scale integration. A separation of time scales naturally arises if either the number of measurement points or the number of simulation points becomes large. Furthermore, at least for one-dimensional problems, we can decouple the harmonic modes between measurement points and solve the fastest part of their dynamics analytically. Our approach is applicable to a wide range of inference problems and is highly parallelizable.
Institute of Scientific and Technical Information of China (English)
Zhang Li-Ping; Xue Ju-Kui; Li Yan-Long
2011-01-01
Both linear and nonlinear excitation in dusty plasmas have been investigated including the nonadiabatic dust charge fluctuation and Gaussian size distribution dust particles.A linear dispersion relation and a Korteweg-de VriesBurgers equation governing the dust acoustic shock waves are obtained.The relevance of the instability of wave and the wave evolution to the dust size distribution and nonadiabatic dust charge fluctuation is illustrated both analytically and numerically.The numerical results show that the Gaussian size distribution of dust particles and the nonadiabatic dust charge fluctuation have strong common influence on the propagation of both linear and nonlinear excitations.
Energy Technology Data Exchange (ETDEWEB)
Eliasson, B., E-mail: bengt.eliasson@strath.ac.uk [SUPA, Physics Department, John Anderson Building, Strathclyde University, Glasgow G4 0NG, Scotland (United Kingdom); Lazar, M., E-mail: mlazar@tp4.rub.de [Centre for Mathematical Plasma Astrophysics, Celestijnenlaan 200B, 3001 Leuven (Belgium); Institut für Theoretische Physik, Lehrstuhl IV: Weltraum- und Astrophysik, Ruhr-Universität Bochum, 44780 Bochum (Germany)
2015-06-15
This paper presents a numerical study of the linear and nonlinear evolution of the electromagnetic electron-cyclotron (EMEC) instability in a bi-Kappa distributed plasma. Distributions with high energy tails described by the Kappa power-laws are often observed in collision-less plasmas (e.g., solar wind and accelerators), where wave-particle interactions control the plasma thermodynamics and keep the particle distributions out of Maxwellian equilibrium. Under certain conditions, the anisotropic bi-Kappa distribution gives rise to plasma instabilities creating low-frequency EMEC waves in the whistler branch. The instability saturates nonlinearly by reducing the temperature anisotropy until marginal stability is reached. Numerical simulations of the Vlasov-Maxwell system of equations show excellent agreement with the growth-rate and real frequency of the unstable modes predicted by linear theory. The wave-amplitude of the EMEC waves at nonlinear saturation is consistent with magnetic trapping of the electrons.
Three types magnetic moment distribution of nonlinear excitations in a Heisenberg helimagnet
Energy Technology Data Exchange (ETDEWEB)
Qi, Jian-Wen [School of Physics, Northwest University, Xi' an 710069 (China); Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi' an 710069 (China); Li, Zai-Dong [Department of Applied Physics, Hebei University of Technology, Tianjin 300401 (China); Yang, Zhan-Ying, E-mail: zyyang@nwu.edu.cn [School of Physics, Northwest University, Xi' an 710069 (China); Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi' an 710069 (China); Yang, Wen-Li [Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi' an 710069 (China); Institute of Modern Physics, Northwest University, Xi' an 710069 (China)
2017-06-15
Highlights: • Three different types of soliton excitations under the spin-wave background are demonstrated in spin chain system. • The magnetic moment distributions corresponding to these solitons are characterized in detail. • The formation mechanisms of those excitations are explained by the magnon density distribution. - Abstract: We study the nonlinear spin dynamics of an anisotropic Heisenberg helimagnet in a fourth-order integrable nonlinear Schrödinger equation. We demonstrate that there are three types of nonlinear spin excitations on a spin-wave background in the Heisenberg helimagnet, notably including anti-dark soliton, W-shaped soliton, and multi-peak soliton. The magnetic moment distribution that corresponds to each of these are characterized in detail. Additionally, the formation mechanism is clarified by the magnon density distribution.
PARAMETER ESTIMATION OF THE HYBRID CENSORED LOMAX DISTRIBUTION
Directory of Open Access Journals (Sweden)
Samir Kamel Ashour
2010-12-01
Full Text Available Survival analysis is used in various fields for analyzing data involving the duration between two events. It is also known as event history analysis, lifetime data analysis, reliability analysis or time to event analysis. One of the difficulties which arise in this area is the presence of censored data. The lifetime of an individual is censored when it cannot be exactly measured but partial information is available. Different circumstances can produce different types of censoring. The two most common censoring schemes used in life testing experiments are Type-I and Type-II censoring schemes. Hybrid censoring scheme is mixture of Type-I and Type-II censoring scheme. In this paper we consider the estimation of parameters of Lomax distribution based on hybrid censored data. The parameters are estimated by the maximum likelihood and Bayesian methods. The Fisher information matrix has been obtained and it can be used for constructing asymptotic confidence intervals.
Control of complex dynamics and chaos in distributed parameter systems
Energy Technology Data Exchange (ETDEWEB)
Chakravarti, S.; Marek, M.; Ray, W.H. [Univ. of Wisconsin, Madison, WI (United States)
1995-12-31
This paper discusses a methodology for controlling complex dynamics and chaos in distributed parameter systems. The reaction-diffusion system with Brusselator kinetics, where the torus-doubling or quasi-periodic (two characteristic incommensurate frequencies) route to chaos exists in a defined range of parameter values, is used as an example. Poincare maps are used for characterization of quasi-periodic and chaotic attractors. The dominant modes or topos, which are inherent properties of the system, are identified by means of the Singular Value Decomposition. Tested modal feedback control schemas based on identified dominant spatial modes confirm the possibility of stabilization of simple quasi-periodic trajectories in the complex quasi-periodic or chaotic spatiotemporal patterns.
Nonlinear Calibration Model Choice between the Four and Five Parameter Logistic Models
Cumberland, William N.; Fong, Youyi; Yu, Xuesong; Defawe, Olivier; Frahm, Nicole; De Rosa, Stephen
2014-01-01
Both the four-parameter logistic (4PL) and the five-parameter logistic (5PL) models are widely used in nonlinear calibration. In this paper, we study the choice between 5PL and 4PL both by the accuracy and precision of the estimated concentrations and by the power to detect an association between a binary disease outcome and the estimated concentrations. Our results show that when the true curve is symmetric around its inflection point, the efficiency loss from using 5PL is negligible under the prevalent experimental design. When the true curve is asymmetric, 4PL may sometimes offer better performance due to bias-variance trade-off. We provide a practical guideline for choosing between 5PL and 4PL and illustrate its application with a real dataset from the HIV Vaccine Trials Network laboratory. PMID:24918306
Institute of Scientific and Technical Information of China (English)
Liu Hailong; Wang Jue; Zheng Chongxun
2007-01-01
Mental task classification is one of the most important problems in Brain-computer interface. This paper studies the classification of five-class mental tasks. The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM (support vector machines). The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments. And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's. The results indicate that the parameter of mean period represents mental tasks well for classification. Furthermore, the method of mean period is less computationally demanding, which indicates its potential use for online BCI systems.
Miksovsky, J.; Raidl, A.
Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
A practical guide to geometric regulation for distributed parameter systems
Aulisa, Eugenio
2015-01-01
A Practical Guide to Geometric Regulation for Distributed Parameter Systems provides an introduction to geometric control design methodologies for asymptotic tracking and disturbance rejection of infinite-dimensional systems. The book also introduces several new control algorithms inspired by geometric invariance and asymptotic attraction for a wide range of dynamical control systems. The first part of the book is devoted to regulation of linear systems, beginning with the mathematical setup, general theory, and solution strategy for regulation problems with bounded input and output operators.
Transfer function modeling of damping mechanisms in distributed parameter models
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
Multivariate meta-analysis for non-linear and other multi-parameter associations
Gasparrini, A; Armstrong, B; Kenward, M G
2012-01-01
In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043
Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution
Directory of Open Access Journals (Sweden)
Azam Zaka
2014-08-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE Nakagami distribution is a flexible life time distribution that may offer a good fit to some failure data sets. It has applications in attenuation of wireless signals traversing multiple paths, deriving unit hydrographs in hydrology, medical imaging studies etc. In this research, we obtain Bayesian estimators of the scale parameter of Nakagami distribution. For the posterior distribution of this parameter, we consider Uniform, Inverse Exponential and Levy priors. The three loss functions taken up are Squared Error Loss function, Quadratic Loss Function and Precautionary Loss function. The performance of an estimator is assessed on the basis of its relative posterior risk. Monte Carlo Simulations are used to compare the performance of the estimators. It is discovered that the PLF produces the least posterior risk when uniform priors is used. SELF is the best when inverse exponential and Levy Priors are used. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}
Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.
Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar
2017-03-01
We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.
A distribution-free newsvendor problem with nonlinear holding cost
Pal, Brojeswar; Sankar Sana, Shib; Chaudhuri, Kripasindhu
2015-05-01
In this paper, we analyse a single-period newsvendor model to determine the optimal order quantity where the customers' balking occurs.This scenario occurs when the customers are opposed to buy a product for various reasons, such as decreasing quality of product, product is not as good as fresh when it reaches under a threshold level, etc. The model is investigated by assuming that the holding cost function depends on order quantity and the inventory level at which customer balking occurs depends on holding cost. The model allows partial backlogging and permits part of the backlogged shortages to turn into lost sales. We develop the model without taking any specific distributional form of demand, only assuming the mean and the variance of the distribution of demand. Finally, we illustrate the model by numerical examples and compare our distribution-free model with the specific distributional form of demand.
Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel
2016-01-01
Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.
Santaren, D.; Peylin, P.; Viovy, N.; Ciais, P.
2003-04-01
Global model of Carbone, water, and energy exchanges between the biosphere and the atmosphere are usually validated and calibrated with intensive measurement made over specific ecosystem like those of the fluxnet networks.However the nonlinear dependance between fluxes and model parameters generally complicate the optimization of the major parameters.In this study, we estimate few key parameters of the ORCHIDEE french model,using diurnal variation measurements of latent heat,sensible heat and net CO2 fluxes for 3 weeks over pine forest (Landes, France).The model is forced with the observed climatic forcing: Temperature, income solar radiations,wind velocity norm, air humidity, pressure and precipitations. We will first present the inverse methodology and the problem linkedto the non linearity. The result of the optimization shows correlations within the initial ensemble of parameters which allow us to choose only five parameters determined independently from the observations. Directly related to the net CO2 flux, the maximum rate of carboxylation,Vcmax,and the stomatal conductance, gs, are significantly changed from their apriori estimate for that period. The aerodynamic resistance, the albedo and a parameter linked to maintenance respiration were also modified within their physical range.Overall the model fit to the data was largely improved. Note however that some discrepancies remain for sensible heat flux which would probably require some model improvements for the stocking of energy in the soil. Such work is currently extended in time to account for parameter variations between the season. The application to other ecosystems and with the supplementary data of the Leaf Area Index will be also discussed.
Optimal vibration control of curved beams using distributed parameter models
Liu, Fushou; Jin, Dongping; Wen, Hao
2016-12-01
The design of linear quadratic optimal controller using spectral factorization method is studied for vibration suppression of curved beam structures modeled as distributed parameter models. The equations of motion for active control of the in-plane vibration of a curved beam are developed firstly considering its shear deformation and rotary inertia, and then the state space model of the curved beam is established directly using the partial differential equations of motion. The functional gains for the distributed parameter model of curved beam are calculated by extending the spectral factorization method. Moreover, the response of the closed-loop control system is derived explicitly in frequency domain. Finally, the suppression of the vibration at the free end of a cantilevered curved beam by point control moment is studied through numerical case studies, in which the benefit of the presented method is shown by comparison with a constant gain velocity feedback control law, and the performance of the presented method on avoidance of control spillover is demonstrated.
Nonlinear systems time-varying parameter estimation: Application to induction motors
Energy Technology Data Exchange (ETDEWEB)
Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, IUT FOTSO Victor, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Ahmed-Ali, Tarek [Ecole Nationale Superieure des Ingenieurs des Etudes et Techniques d' Armement (ENSIETA), 2 Rue Francois Verny, 29806 Brest Cedex 9 (France); Lamnabhi-Lagarrigue, F. [Laboratoire des Signaux et Systemes (L2S), C.N.R.S-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)
2008-11-15
In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines. (author)
C -parameter distribution at N3LL' including power corrections
Hoang, André H.; Kolodrubetz, Daniel W.; Mateu, Vicent; Stewart, Iain W.
2015-05-01
We compute the e+e- C -parameter distribution using the soft-collinear effective theory with a resummation to next-to-next-to-next-to-leading-log prime accuracy of the most singular partonic terms. This includes the known fixed-order QCD results up to O (αs3), a numerical determination of the two-loop nonlogarithmic term of the soft function, and all logarithmic terms in the jet and soft functions up to three loops. Our result holds for C in the peak, tail, and far tail regions. Additionally, we treat hadronization effects using a field theoretic nonperturbative soft function, with moments Ωn. To eliminate an O (ΛQCD) renormalon ambiguity in the soft function, we switch from the MS ¯ to a short distance "Rgap" scheme to define the leading power correction parameter Ω1. We show how to simultaneously account for running effects in Ω1 due to renormalon subtractions and hadron-mass effects, enabling power correction universality between C -parameter and thrust to be tested in our setup. We discuss in detail the impact of resummation and renormalon subtractions on the convergence. In the relevant fit region for αs(mZ) and Ω1, the perturbative uncertainty in our cross section is ≃ 2.5 % at Q =mZ.
Inversion of generalized relaxation time distributions with optimized damping parameter
Florsch, Nicolas; Revil, André; Camerlynck, Christian
2014-10-01
Retrieving the Relaxation Time Distribution (RDT), the Grains Size Distribution (GSD) or the Pore Size Distribution (PSD) from low-frequency impedance spectra is a major goal in geophysics. The “Generalized RTD” generalizes parametric models like Cole-Cole and many others, but remains tricky to invert since this inverse problem is ill-posed. We propose to use generalized relaxation basis function (for instance by decomposing the spectra on basis of generalized Cole-Cole relaxation elements instead of the classical Debye basis) and to use the L-curve approach to optimize the damping parameter required to get smooth and realistic inverse solutions. We apply our algorithm to three examples, one synthetic and two real data sets, and the program includes the possibility of converting the RTD into GSD or PSD by choosing the value of the constant connecting the relaxation time to the characteristic polarization size of interest. A high frequencies (typically above 1 kHz), a dielectric term in taken into account in the model. The code is provided as an open Matlab source as a supplementary file associated with this paper.
Distributed Fuzzy and Stochastic Observers for Nonlinear Systems
Lendek, Z.
2009-01-01
Many problems in decision making, control, and monitoring require that all variables of interest, usually states and parameters of the system, are known at all times. However, in practical situations, not all variables are measurable or they are not measured due to technical or economical reasons. T
Cai, Lanlan; Li, Peng; Luo, Qi; Zhai, Pengcheng; Zhang, Qingjie
2017-03-01
As no single thermoelectric material has presented a high figure-of-merit (ZT) over a very wide temperature range, segmented thermoelectric generators (STEGs), where the p- and n-legs are formed of different thermoelectric material segments joined in series, have been developed to improve the performance of thermoelectric generators. A crucial but difficult problem in a STEG design is to determine the optimal values of the geometrical parameters, like the relative lengths of each segment and the cross-sectional area ratio of the n- and p-legs. Herein, a multi-parameter and nonlinear optimization method, based on the Improved Powell Algorithm in conjunction with the discrete numerical model, was implemented to solve the STEG's geometrical optimization problem. The multi-parameter optimal results were validated by comparison with the optimal outcomes obtained from the single-parameter optimization method. Finally, the effect of the hot- and cold-junction temperatures on the geometry optimization was investigated. Results show that the optimal geometry parameters for maximizing the specific output power of a STEG are different from those for maximizing the conversion efficiency. Data also suggest that the optimal geometry parameters and the interfacial temperatures of the adjacent segments optimized for maximum specific output power or conversion efficiency vary with changing hot- and cold-junction temperatures. Through the geometry optimization, the CoSb3/Bi2Te3-based STEG can obtain a maximum specific output power up to 1725.3 W/kg and a maximum efficiency of 13.4% when operating at a hot-junction temperature of 823 K and a cold-junction temperature of 298 K.
Directory of Open Access Journals (Sweden)
Alexander Yuryevich Meigal
2015-07-01
Full Text Available We compared a set of surface EMG (sEMG parameters in several groups of schizophrenia (SZ, n=74 patients and healthy controls (n=11 and coupled them with the clinical data. sEMG records were quantified with spectral, mutual information (MI based and recurrence quantification analysis (RQA parameters, and with approximate and sample entropies (ApEn and SampEn. Psychotic deterioration was estimated with Positive and Negative Syndrome Scale (PANSS and with the positive subscale of PANSS. Neuroleptic-induced parkinsonism (NIP motor symptoms were estimated with Simpson-Angus Scale (SAS. Dyskinesia was measured with Abnormal Involuntary Movement Scale (AIMS. We found that there was no difference in values of sEMG parameters between healthy controls and drug-naïve SZ patients.The most specific group was formed of SZ patients who were administered both typical and atypical antipsychotics (AP. Their sEMG parameters were significantly different from those of SZ patients taking either typical or atypical AP or taking no AP. This may represent a kind of synergistic effect of these two classes of AP. For the clinical data we found that PANSS, SAS, and AIMS were not correlated to any of the sEMG parameters. Conclusion: with nonlinear parameters of sEMG it is possible to reveal NIP in SZ patients, and it may help to discriminate between different clinical groups of SZ patients. Combined typical and atypical AP therapy has stronger effect on sEMG than a therapy with AP of only one class.
Institute of Scientific and Technical Information of China (English)
WANG Bo; HUO Zhenhua
2013-01-01
An extension of the conditional nonlinear optimal parameter perturbation (CNOP-P) method is applied to the parameter optimization of the Common Land Model (CoLM) for the North China Plain with the differential evolution (DE) method.Using National Meteorological Center (NMC) Reanalysis 6-hourly surface flux data and National Center for Environmental Prediction/Department of Energy (NCEP/DOE)Atmospheric Model Intercomparison Project II (AMIP-II) 6-hourly Reanalysis Gaussian Grid data,two experiments (I and II) were designed to investigate the impact of the percentages of sand and clay in the shallow soil in CoLM on its ability to simulate shallow soil moisture.A third experiment (III) was designed to study the shallow soil moisture and latent heat flux simultaneously.In all the three experiments,after the optimization stage,the percentages of sand and clay of the shallow soil were used to predict the shallow soil moisture in the following month.The results show that the optimal parameters can enable CoLM to better simulate shallow soil moisture,with the simulation results of CoLM after the double-parameter optimal experiment being better than the single-parameter optimal experiment in the optimization slot.Furthermore,the optimal parameters were able to significantly improve the prediction results of CoLM at the prediction stage.In addition,whether or not the atmospheric forcing and observational data are accurate can seriously affect the results of optimization,and the more accurate the data are,the more significant the results of optimization may be.
Surface and Atmospheric Parameter Retrieval From AVIRIS Data: The Importance of Non-Linear Effects
Green Robert O.; Moreno, Jose F.
1996-01-01
AVIRIS data represent a new and important approach for the retrieval of atmospheric and surface parameters from optical remote sensing data. Not only as a test for future space systems, but also as an operational airborne remote sensing system, the development of algorithms to retrieve information from AVIRIS data is an important step to these new approaches and capabilities. Many things have been learned since AVIRIS became operational, and the successive technical improvements in the hardware and the more sophisticated calibration techniques employed have increased the quality of the data to the point of almost meeting optimum user requirements. However, the potential capabilities of imaging spectrometry over the standard multispectral techniques have still not been fully demonstrated. Reasons for this are the technical difficulties in handling the data, the critical aspect of calibration for advanced retrieval methods, and the lack of proper models with which to invert the measured AVIRIS radiances in all the spectral channels. To achieve the potential of imaging spectrometry, these issues must be addressed. In this paper, an algorithm to retrieve information about both atmospheric and surface parameters from AVIRIS data, by using model inversion techniques, is described. Emphasis is put on the derivation of the model itself as well as proper inversion techniques, robust to noise in the data and an inadequate ability of the model to describe natural variability in the data. The problem of non-linear effects is addressed, as it has been demonstrated to be a major source of error in the numerical values retrieved by more simple, linear-based approaches. Non-linear effects are especially critical for the retrieval of surface parameters where both scattering and absorption effects are coupled, as well as in the cases of significant multiple-scattering contributions. However, sophisticated modeling approaches can handle such non-linear effects, which are especially
Energy Technology Data Exchange (ETDEWEB)
Gul-e-Ali,; Mirza, Arshad M. [Theoretical Plasma Physics Group, Physics Department, Quaid-i-Azam University, Islamabad 45320 (Pakistan); Masood, W. [COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad 44000 (Pakistan); National Centre for Physics, Shahdara Valley Road, Islamabad (Pakistan)
2016-02-15
Coupling of drift vortex and the ion acoustic modes in the linear and nonlinear regimes are investigated with sheared ion flow perpendicular to the ambient magnetic field in a plasma comprising of hot ions and nonthermal population of electrons. In this regard, generation of nonlinear vortex structures in the presence of kappa, Cairns, and q-nonextensive electron distributions are investigated in detail, and comparison with the Maxwellian distribution is also made. The appositeness of the present investigation in the matter of auroral F-region is also pointed out.
Distributed Cooperative Control of Nonlinear and Non-identical Multi-agent Systems
DEFF Research Database (Denmark)
Bidram, Ali; Lewis, Frank; Davoudi, Ali
2013-01-01
to the synchronization problem for an identical linear multi-agent system. The controller for each agent is designed to be fully distributed, such that each agent only requires its own information and the information of its neighbors. The proposed control method is exploited to implement the secondary voltage control......This paper exploits input-output feedback linearization technique to implement distributed cooperative control of multi-agent systems with nonlinear and non-identical dynamics. Feedback linearization transforms the synchronization problem for a nonlinear and heterogeneous multi-agent system...... for electric power microgrids. The effectiveness of the proposed control is verified by simulating a microgrid test system....
Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.
Wang, Xinghu; Hong, Yiguang; Ji, Haibo
2016-07-01
The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.
Motiei, H.; Jafari, A.; Naderali, R.
2017-02-01
In this paper, two chemically synthesized organic azo dyes, 2-(2,5-Dichloro-phenyazo)-5,5-dimethyl-cyclohexane-1,3-dione (azo dye (i)) and 5,5-Dimethyl-2-tolylazo-cyclohexane-1,3-dione (azo dye (ii)), have been studied from optical Kerr nonlinearity point of view. These materials were characterized by Ultraviolet-visible spectroscopy. Experiments were performed using a continous wave diode-pumped laser at 532 nm wavelength in three intensities of the laser beam. Nonlinear absorption (β), refractive index (n2) and third-order susceptibility (χ (3)) of dyes, were calculated. Nonlinear absorption coefficient of dyes have been calculated from two methods; 1) using theoretical fits and experimental data in the Z-scan technique, 2) using the strength of nonlinearity curves. The values of β obtained from both of the methods were approximately the same. The results demonstrated that azo dye (ii) displays better nonlinearity and has a lower two-photon absorption threshold than azo dye (i). Calculated parameter related to strength of nonlinearity for azo dye (ii) was higher than azo dye (i), It may be due to presence of methyl in azo dye (ii) instead of chlorine in azo dye (i). Furthermore, The measured values of third order susceptibility of azo dyes were from the order of 10-9 esu . These azo dyes can be suitable candidate for optical switching devices.
On a six-parameter generalized Burr XII distribution
A. K. Olapade
2008-01-01
In this paper, we derive a probability density function that generalizes the Burr XII distribution. The cumulative distribution function and the $n^{th}$ moment of the generalized distribution are obtained while the distribution of some order statistics of the distribution are established. A theorem that relate the new distribution to another statistical distribution is established.
Directory of Open Access Journals (Sweden)
Gongsheng Li
2011-01-01
Full Text Available A real undisturbed soil-column infiltrating experiment in Zibo, Shandong, China, is investigated, and a nonlinear transport model for a solute ion penetrating through the column is put forward by using nonlinear Freundlich's adsorption isotherm. Since Freundlich's exponent and adsorption coefficient and source/sink terms in the model cannot be measured directly, an inverse problem of determining these parameters is encountered based on additional breakthrough data. Furthermore, an optimal perturbation regularization algorithm is introduced to determine the unknown parameters simultaneously. Numerical simulations are carried out and then the inversion algorithm is applied to solve the real inverse problem and reconstruct the measured data successfully. The computational results show that the nonlinear advection-dispersion equation discussed in this paper can be utilized by hydrogeologists to research solute transport behaviors with nonlinear adsorption in porous medium.
Dynamical parton distributions from DGLAP equations with nonlinear corrections
Wang, Rong
2016-01-01
Determination of proton parton distribution functions is present under the dynamical parton model assumption by applying DGLAP equations with GLR-MQ-ZRS corrections. We provide two data sets, referred as IMParton16, which are from two different nonperturbative inputs. One is the naive three valence quarks input and the other is three valence quarks with flavor-asymmetric sea components input. Basically, both data sets are compatible with the experimental measurements at high scale ($Q^2>2$ GeV$^2$). Furthermore, our analysis shows that the input with flavor-asymmetric sea components better reproduce the structure functions at high $Q^2$. Generally, the obtained parton distribution functions, especially the gluon distribution functions, are the good options of inputs for simulations of high energy scattering processes. The analysis is performed under the fixed-flavor number scheme for $n_f=$ 3, 4, 5 and uses the $\\overline{\\text MS}$ scheme for the running coupling $\\alpha_s$ and the heavy-quark masses. Both d...
A comparative study of non-linearity parameter for binary liquid mixtures
Indian Academy of Sciences (India)
J D Pandey; Ranjan Dey; Vinay Sanguri; Jyotsna Chhabra; Tanuja Nautiyal
2005-09-01
The present investigation comprises of theoretical evaluation of acoustic non-linearity parameter, / for equimolar binary mixtures, viz. chlorobenzene or 1-chloronaphthalene with a series of normal alkanes (n-C, = 6, 8, 10, 12, 14, 16), and with a series of highly branched alkanes (br-C, = 6, 8, 12, 16), viz. 2,2-dimethylbutane (br-C6), 2,2,4-trimethylpentane (br-C8), 2,2,4,6,6-pentamethylheptane (br-C12) and 2,2,4,4,6,8,8-heptamethylnonane (br-C16). Tong and Dong method, ther- moacoustical method, Hartmann relation and Ballou relation have been employed to evaluate /. A comparative study of / values obtained from the aforementioned methods has been made. The results are discussed on the basis of structural orientations of normal and branched alkanes.
A study on biological media of ultrasonic nonlinear parameter B/A
Energy Technology Data Exchange (ETDEWEB)
Kim, Jeong Koo; Jung, Hong Ryang; Lim, Cheong Hwan [Hanseo Univ, Seosan (Korea, Republic of)
2001-06-01
This study deals with the relationship between the magnitude of ultrasonic nonlinear parameter B/A, and sound speed of amount of fat present in biological media to measure B/A system using a wide band ultrasonic transducer. To represent this case, mixtures of egg whites and egg yolk were studied. Even though the differences in density and sound speed of the two egg components were in the range of 1%, B/A increase parabolically as a function of the fat density, which is not in agreement with the Yoshizumi et al.'s suggestion. In skim, milk that dose not contain fat, both the B/A and the sound speed increase with the solubility. It is considered that protein could affect these values.
Study of Heart Rate Variability in bipolar disorder: linear and nonlinear parameters during sleep
Directory of Open Access Journals (Sweden)
Matteo eMigliorini
2012-01-01
Full Text Available In the present paper we propose a methodology for the assessment of the autonomic nervous system (ANS in patients affected by bipolar disorder. ANS was explored by means heart rate variability (HRV analysis carried out during night recordings through the evaluation of many different parameters in the time and in the frequency domain, linear and non-linear. The recording of the signals was performed by a wearable sensorized T-shirt. HRV with movement analysis allowed also sleep staging and the estimation of REM sleep percentage over the total sleep time. A group of 8 normal female constituted the control group, on which normality ranges were estimated. One pathologic subject was recorded during four different nights, at time intervals of at least one week, and during different phases of the disturbance. Some of the calculated parameters (MEANNN, SDNN, RMSSD confirmed reduced HRV in depression and bipolar disorder. REM sleep percentage was found to be increased. LZC (Lempel Ziv complexity and SampEn (Sample Entropy, on the other hand, seem to correlate with the depression level. Even if the number of examined subjects is small, and the results need further validation, the proposed methodology and the calculated parameters seem promising tools for the monitoring of mood changes in psychiatric disorders.
Choi, Yun Ho; Yoo, Sung Jin
2017-03-28
A minimal-approximation-based distributed adaptive consensus tracking approach is presented for strict-feedback multiagent systems with unknown heterogeneous nonlinearities and control directions under a directed network. Existing approximation-based consensus results for uncertain nonlinear multiagent systems in lower-triangular form have used multiple function approximators in each local controller to approximate unmatched nonlinearities of each follower. Thus, as the follower's order increases, the number of the approximators used in its local controller increases. However, the proposed approach employs only one function approximator to construct the local controller of each follower regardless of the order of the follower. The recursive design methodology using a new error transformation is derived for the proposed minimal-approximation-based design. Furthermore, a bounding lemma on parameters of Nussbaum functions is presented to handle the unknown control direction problem in the minimal-approximation-based distributed consensus tracking framework and the stability of the overall closed-loop system is rigorously analyzed in the Lyapunov sense.
Influence of Geant4 parameters on proton dose distribution
Directory of Open Access Journals (Sweden)
Asad Merouani
2015-09-01
Full Text Available Purpose: The proton therapy presents a great precision during the radiation dose delivery. It is useful when the tumor is located in a sensitive area like brain or eyes. The Monte Carlo (MC simulations are usually used in treatment planning system (TPS to estimate the radiation dose. In this paper we are interested in estimating the proton dose statistical uncertainty generated by the MC simulations. Methods: Geant4 was used in the simulation of the eye’s treatment room for 62 MeV protons therapy, installed in the Istituto Nazionale Fisica Nucleare Laboratori Nazionali del Sud (LNS-INFN facility in Catania. This code is a Monte Carlo based on software dedicated to simulate the passage of particles through the matter. In this work, we are interested in optimizing the Geant4 parameters on energy deposit distribution by proton to achieve the spatial resolution of dose distribution required for cancer therapy. We propose various simulations and compare the corresponding dose distribution inside water to evaluate the statistical uncertainties. Results: The simulated Bragg peak, based on facility model is in agreement with the experimental data, The calculations show that the mean statistical uncertainty is less than 1% for a simulation set with 5 × 104 events, 10-3 mm production threshold and a 10-2 mm step limit. Conclusion: The set of Geant4 cut and step limit values can be chosen in combination with the number of events to reach precision recommended from International Commission on Radiation Units and measurements (ICRU in Monte Carlo codes for proton therapy treatment.
Tang, Ze; Park, Ju H.; Lee, Tae H.
2016-10-01
This paper is devoted to the cluster synchronization issue of nonlinearly coupled Lur'e networks under the distributed adaptive pinning control strategy. The time-varying delayed networks consisted of identical and nonidentical Lur'e systems are discussed respectively by applying the edge-based pinning control scheme. In each cluster, the edges belonging to the spanning tree are pinned. In view of the nonlinearly couplings of the networks, for the first time, an efficient distributed nonlinearly adaptive update law based on the local information of the dynamical behaviors of node is proposed. Sufficient criteria for the achievement of cluster synchronization are derived based on S-procedure, Kronecker product and Lyapunov stability theory. Additionally, some illustrative examples are provided to demonstrate the effectiveness of the theoretical results.
Output Feedback Distributed Containment Control for High-Order Nonlinear Multiagent Systems.
Li, Yafeng; Hua, Changchun; Wu, Shuangshuang; Guan, Xinping
2017-01-31
In this paper, we study the problem of output feedback distributed containment control for a class of high-order nonlinear multiagent systems under a fixed undirected graph and a fixed directed graph, respectively. Only the output signals of the systems can be measured. The novel reduced order dynamic gain observer is constructed to estimate the unmeasured state variables of the system with the less conservative condition on nonlinear terms than traditional Lipschitz one. Via the backstepping method, output feedback distributed nonlinear controllers for the followers are designed. By means of the novel first virtual controllers, we separate the estimated state variables of different agents from each other. Consequently, the designed controllers show independence on the estimated state variables of neighbors except outputs information, and the dynamics of each agent can be greatly different, which make the design method have a wider class of applications. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed method.
On the joint distribution of surface slopes for the fourth order nonlinear random sea waves
Institute of Scientific and Technical Information of China (English)
张书文; 孙孚; 管长龙
1999-01-01
Based upon the nonlinear model of Longuet-Higgins the joint distribution of wave surface slopes is theoretically investigated. It is shown that in the fourth order approximation, the distribution is given by truncated Gram-Charlier series. The types of wave-wave coupling interactions are related to the order of approximation to nonlinearity of sea surface, which eventually defines the truncated term of the Gram-Charlier series. For each order approximation, the coefficients in the series are modified comparatively to the corresponding ones for the previous order approximation. If the nonlinear effect of the kurtosis is considered, the wave surface must be as accurate at least as to the third order approximation, and with regard to skewness, the wave surface must be as accurate at least as to the fourth order approximation.
Kramer, Sean; Bollt, Erik M
2013-09-01
Given multiple images that describe chaotic reaction-diffusion dynamics, parameters of a partial differential equation (PDE) model are estimated using autosynchronization, where parameters are controlled by synchronization of the model to the observed data. A two-component system of predator-prey reaction-diffusion PDEs is used with spatially dependent parameters to benchmark the methods described. Applications to modeling the ecological habitat of marine plankton blooms by nonlinear data assimilation through remote sensing are discussed.
Bollt, Erik
2012-01-01
Given multiple images that describe chaotic reaction-diffusion dynamics, parameters of a PDE model are estimated using autosynchronization, where parameters are controlled by synchronization of the model to the observed data. A two-component system of predator-prey reaction-diffusion PDEs is used with spatially dependent parameters to benchmark the methods described. Applications to modelling the ecological habitat of marine plankton blooms by nonlinear data assimilation through remote sensing is discussed.
Directory of Open Access Journals (Sweden)
Mkrtychev Oleg Vartanovich
Full Text Available In the article the problem of calculation of a construction basis system in case of earthquake is considered taking into account casual properties of basis soil in various points of the soil body. As a stochastic function in the calculation of linearly deformable basis, the deformation module, which accepts different values in the direction x, y, z, was chosen. In the calculation of the system on non-linearly deformable basis as incidentally distributed sizes the following parameters were accepted: deformation module, shear modulus, specific adhesion, angle of internal friction. The authors of the article offer to consider initial seismic influence in the form of casual stationary process. In order to solve such problems modern software systems are proposed that solve differential equations of motion via direct integration with explicit schemes. The calculation in this case will be held on the synthesized accelerograms. A short review of the task solution of the beam lying on elastic basis, which was received by D.N. Sobolev at casual distribution of pastel coefficient in the direction x, is provided in article. In order to define the objective, D.N. Sobolev gives expressions for a population mean and correlation function of stochastic function. As a result of the task solution population means and dispersions of function of movements and its derivatives were received. The problem formulation considered in the article is more complicated, but at the same time important from a practical standpoint.
Energy Distribution of a Regular Black Hole Solution in Einstein-Nonlinear Electrodynamics
Directory of Open Access Journals (Sweden)
I. Radinschi
2015-01-01
Full Text Available A study about the energy momentum of a new four-dimensional spherically symmetric, static and charged, regular black hole solution developed in the context of general relativity coupled to nonlinear electrodynamics is presented. Asymptotically, this new black hole solution behaves as the Reissner-Nordström solution only for the particular value μ=4, where μ is a positive integer parameter appearing in the mass function of the solution. The calculations are performed by use of the Einstein, Landau-Lifshitz, Weinberg, and Møller energy momentum complexes. In all the aforementioned prescriptions, the expressions for the energy of the gravitating system considered depend on the mass M of the black hole, its charge q, a positive integer α, and the radial coordinate r. In all these pseudotensorial prescriptions, the momenta are found to vanish, while the Landau-Lifshitz and Weinberg prescriptions give the same result for the energy distribution. In addition, the limiting behavior of the energy for the cases r→∞, r→0, and q=0 is studied. The special case μ=4 and α=3 is also examined. We conclude that the Einstein and Møller energy momentum complexes can be considered as the most reliable tools for the study of the energy momentum localization of a gravitating system.
Nonlinear plasma processes and the formation of electron kappa distribution
Yoon, Peter
2016-07-01
The goal of nonequilibrium statistical mechanics is to establish fundamental relationship between the time irreversible macroscopic dynamics and the underlying time reversible behavior of microscopic system. The paradigm of achieving this seemingly paradoxical goal is through the concept of probability. For classical systems Boltzmann accomplished this through his H theorem and his kinetic equation for dilute gas. Boltzmann's H function is the same as classical extensive entropy aside from the minus sign, and his kinetic equation is applicable for short-range molecular interaction. For plasmas, the long-range electromagnetic force dictates the inter-particular interaction, and the underlying entropy is expected to exhibit non-extensive, or non-additive behavior. Among potential models for the non-additive entropy, the celebrated Tsallis entropy is the most well known. One of the most useful fundamental kinetic equations that governs the long-range plasma interaction is that of weak turbulence kinetic theory. At present, however, there is no clear-cut connection between the Tsallis entropy and the kinetic equations that govern plasma behavior. This can be contrasted to Boltzmann's H theorem, which is built upon his kinetic equation. The best one can do is to show that the consequences of Tsallis entropy and plasma kinetic equation are the same, that is, they both imply kappa distribution. This presentation will overview the physics of electron acceleration by beam-generated Langmuir turbulence, and discuss the asymptotic solution that rigorously can be shown to correspond to the kappa distribution. Such a finding is a strong evidence, if not water-tight proof, that there must be profound inter-relatioship between the Tsallis thermostatistical theory and the plasma kinetic theory.
Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings.
Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen
2013-02-01
In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.
Modified Nonlinear Inverse Synthesis for Optical Links with Distributed Raman Amplification
Le, Son T; Rosa, Pawel; Ania-Castanon, Juan D; Turitsyn, Sergei K
2015-01-01
Nonlinear Fourier transform (NFT) and eigenvalue communication with the use of nonlinear signal spectrum (both discrete and continuous), have been recently discussed as a promising transmission method to combat fiber nonlinearity impairments. However, because the NFT-based transmission method employs the integrability property of the lossless nonlinear Schr\\"odinger equation (NLSE), the original approach can only be applied directly to optical links with ideal distributed Raman amplification. In this paper, we investigate in details the impact of a non-ideal Raman gain profile on the performance of the nonlinear inverse synthesis (NIS) scheme, in which the transmitted information is encoded directly onto the continuous part of the nonlinear signal spectrum. We propose the lossless path-averaged (LPA) model for fiber links with non-ideal Raman gain profile by taking into account the average effect of the Raman gain. We show that the NIS scheme employing the LPA model can offer a performance gain of 3 dB regard...
Non-linear parameter estimation for the LTP experiment: analysis of an operational exercise
Congedo, G; Ferraioli, L; Hueller, M; Vitale, S; Hewitson, M; Nofrarias, M; Monsky, A; Armano, M; Grynagier, A; Diaz-Aguilo, M; Plagnol, E; Rais, B
2011-01-01
The precursor ESA mission LISA-Pathfinder, to be flown in 2013, aims at demonstrating the feasibility of the free-fall, necessary for LISA, the upcoming space-born gravitational wave observatory. LISA Technology Package (LTP) is planned to carry out a number of experiments, whose main targets are to identify and measure the disturbances on each test-mass, in order to reach an unprecedented low-level residual force noise. To fulfill this plan, it is then necessary to correctly design, set-up and optimize the experiments to be performed on-flight and do a full system parameter estimation. Here we describe the progress on the non-linear analysis using the methods developed in the framework of the \\textit{LTPDA Toolbox}, an object-oriented MATLAB Data Analysis environment: the effort is to identify the critical parameters and remove the degeneracy by properly combining the results of different experiments coming from a closed-loop system like LTP.
Kumar, K Vasanth; Sivanesan, S
2005-08-31
Comparison analysis of linear least square method and non-linear method for estimating the isotherm parameters was made using the experimental equilibrium data of safranin onto activated carbon at two different solution temperatures 305 and 313 K. Equilibrium data were fitted to Freundlich, Langmuir and Redlich-Peterson isotherm equations. All the three isotherm equations showed a better fit to the experimental equilibrium data. The results showed that non-linear method could be a better way to obtain the isotherm parameters. Redlich-Peterson isotherm is a special case of Langmuir isotherm when the Redlich-Peterson isotherm constant g was unity.
Bagheri Tolabi, Hajar; Hosseini, Rahil; Shakarami, Mahmoud Reza
2016-06-01
This article presents a novel hybrid optimization approach for a nonlinear controller of a distribution static compensator (DSTATCOM). The DSTATCOM is connected to a distribution system with the distributed generation units. The nonlinear control is based on partial feedback linearization. Two proportional-integral-derivative (PID) controllers regulate the voltage and track the output in this control system. In the conventional scheme, the trial-and-error method is used to determine the PID controller coefficients. This article uses a combination of a fuzzy system, simulated annealing (SA) and intelligent water drops (IWD) algorithms to optimize the parameters of the controllers. The obtained results reveal that the response of the optimized controlled system is effectively improved by finding a high-quality solution. The results confirm that using the tuning method based on the fuzzy-SA-IWD can significantly decrease the settling and rising times, the maximum overshoot and the steady-state error of the voltage step response of the DSTATCOM. The proposed hybrid tuning method for the partial feedback linearizing (PFL) controller achieved better regulation of the direct current voltage for the capacitor within the DSTATCOM. Furthermore, in the event of a fault the proposed controller tuned by the fuzzy-SA-IWD method showed better performance than the conventional controller or the PFL controller without optimization by the fuzzy-SA-IWD method with regard to both fault duration and clearing times.
Institute of Scientific and Technical Information of China (English)
Wenhua Cao; Songhao Liu
2005-01-01
Stable picosecond soliton transmission is demonstrated numerically by use of concatenated gain-distributed nonlinear amplifying fiber loop mirrors (NALMs). We show that, as compared with previous soliton transmission schemes that use conventional NALMs or nonlinear optical loop mirror (NOLM) and amplifier combinations, the present scheme permits significant increase of loop-mirror (amplifier) spacing. The broad switching window of the present device and the high quality pulses switched from it provide a reasonable stability range for soliton transmission. Soliton-soliton interactions can be reduced efficiently by using lowly dispersive fibers.
Semigroup approximation and robust stabilization of distributed parameter systems
Kurdila, A. J.; Fabiano, R.; Strganac, T.; Hsu, S.
1994-01-01
Theoretical results that enable rigorous statements of convergence and exponential stability of Galerkin approximations of LQR controls for infinite dimensional, or distributed parameter, systems have proliferated over the past ten years. In addition, extensive progress has been made over the same time period in the derivation of robust control design strategies for finite dimensional systems. However, the study of the convergence of robust finite dimensional controllers to robust controllers for infinite dimensional systems remains an active area of research. We consider a class of soft-constrained differential games evolving in a Hilbert space. Under certain conditions, a saddle point control can be given in feedback form in terms of a solution to a Riccati equation. By considering a related LQR problem, we can show a convergence result for finite dimensional approximations of this differential game. This yields a computational algorithm for the feedback gain that can be derived from similar strategies employed in infinite dimensional LQR control design problems. The approach described in this paper also inherits the additional properties of stability robustness common to game theoretic methods in finite dimensional analysis. These theoretical convergence and stability results are verified in several numerical experiments.
Institute of Scientific and Technical Information of China (English)
MA Tao; ZHANG Weigang; ZHANG Yang; TANG Ting
2015-01-01
The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it’s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design.
Multivariate phase type distributions - Applications and parameter estimation
DEFF Research Database (Denmark)
Meisch, David
and reducing model uncertainties. Research has shown that the errors on cost estimates for infrastructure projects clearly do not follow a normal distribution but is skewed towards cost overruns. This skewness can be described using phase type distributions. Cost benefit analysis assesses potential future...... to the class of phase type distributions. Phase type distributions have several advantages. They are versatile in the sense that they can be used to approximate any given probability distribution on the positive reals. There exist general probabilistic results for the entire class of phase type distributions......, allowing for different estimation methods for the whole class or subclasses of phase type distributions. These attributes make this class of distributions an interesting alternative to the normal distribution. When facing multivariate problems, the only general distribution that allows for estimation...
Jeong, Hyunjo; Zhang, Shuzeng; Li, Xiongbing
2017-02-01
In this work, we employ a focused beam theory to modify the phase reversal at the stress-free boundary, and consequently enhance the second harmonic generation during its back-propagation toward the initial source position. We first confirmed this concept through experiment by using a spherically focused beam at the water-air interface, and measuring the reflected second harmonic and comparing with a planar wave reflected from the same stress-free or a rigid boundary. In order to test the feasibility of this idea for measuring the nonlinearity parameter of solids in a reflection mode, a focused nonlinear ultrasonic beam is modeled for focusing at and reflection from a stress-free boundary. A nonlinearity parameter expression is then defined together with diffraction and attenuation corrections.
Dynamics, effciency and energy distribution of nonlinear plasmon-assisted generation of hot carriers
Demichel, O; Viarbitskaya, S; Mejard, R; de Fornel, F; Hertz, E; Billard, F; Bouhelier, A; Cluzel, B
2016-01-01
We employ nonlinear autocorrelation measurements to investigate plasmon-assisted hot carrier dynamics generated in optical gold antennas. We demonstrate that surface plasmons enable a nonlinear formation of hot carriers, providing thus a unique lever to optimize the energy distribution and generation efficiency of the photo-excited charges. The temporal response of the carriers' relaxation can be controlled within a range extending from 500~fs to 2.5~ps. By conducting a quantitative analysis of the dynamics, we determine the nonlinear absorption cross-section of individual optical antennas. As such, this work provides strong insights on the understanding of plasmon-induced hot carrier generation, especially in the view of applications where the time response plays a preponderant role.
DEFF Research Database (Denmark)
Backi, Christoph Josef; Bendtsen, Jan Dimon; Leth, John-Josef
2014-01-01
In this work the stability properties of a nonlinear partial differential equation (PDE) with state–dependent parameters is investigated. Among other things, the PDE describes freezing of foodstuff, and is closely related to the (Potential) Burgers’ Equation. We show that for certain forms...
Energy Technology Data Exchange (ETDEWEB)
Lee, Kyoung Jun; Kim, Jong Beom; Song, Dong Gil; Jhang, Kyung Young [Dept. of Mechanical Engineering, Hanyang University, Seoul (Korea, Republic of)
2015-08-15
In ultrasonic nonlinear parameter measurement using the fast Fourier transform(FFT) of tone-burst signals, the side lobe and leakage on spectrum because of finite time and non-periodicity of signals makes it difficult to measure the harmonic magnitudes accurately. The window function made it possible to resolve this problem. In this study, the effect of the Hanning and Turkey window functions on the experimental measurement of nonlinear parameters was analyzed. In addition, the effect of changes in tone burst signal number with changes in the window function on the experimental measurement was analyzed. The result for both window functions were similar and showed that they enabled reliable nonlinear parameter measurement. However, in order to restore original signal amplitude, the amplitude compensation coefficient should be considered for each window function. On a separate note, the larger number of tone bursts was advantageous for stable nonlinear parameter measurement, but this effect was more advantageous in the case of the Hanning window than the Tukey window.
Energy Technology Data Exchange (ETDEWEB)
Jilani, Asim, E-mail: asim.jilane@gmail.com [Centre of Nanotechnology, King Abdulaziz University, Jeddah (Saudi Arabia); Abdel-wahab, M.Sh [Centre of Nanotechnology, King Abdulaziz University, Jeddah (Saudi Arabia); Materials Science and Nanotechnology Department, Faculty of Postgraduate Studies for Advanced Sciences, Beni -Suef University, Beni-Suef (Egypt); Al-ghamdi, Attieh A. [Centre of Nanotechnology, King Abdulaziz University, Jeddah (Saudi Arabia); Dahlan, Ammar sadik [Department of architecture, faculty of environmental design, King Abdulaziz University, Jeddah (Saudi Arabia); Yahia, I.S. [Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha (Saudi Arabia); Nano-Science & Semiconductor Labs, Department of Physics, Faculty of Education, Ain Shams University, Roxy, 11757 Cairo (Egypt)
2016-01-15
The 2.2 wt% of aluminum (Al)-doped zinc oxide (AZO) transparent and preferential c-axis oriented thin films were prepared by using radio frequency (DC/RF) magnetron sputtering at different substrate temperature ranging from room temperature to 200 °C. For structural analysis, X-ray Diffraction (XRD) and Atomic Force Electron Microscope (AFM) was used for morphological studies. The optical parameters such as, optical energy gap, refractive index, extinction coefficient, dielectric loss, tangent loss, first and third order nonlinear optical properties of transparent films were investigated. High transmittance above 90% and highly homogeneous surface were observed in all samples. The substrate temperature plays an important role to get the best transparent conductive oxide thin films. The substrate temperature at 150 °C showed the growth of highly transparent AZO thin film. Energy gap increased with the increased in substrate temperature of Al doped thin films. Dielectric constant and loss were found to be photon energy dependent with substrate temperature. The change in substrate temperature of Al doped thin films also affect the non-liner optical properties of thin films. The value of χ{sup (3)} was found to be changed with the grain size of the thin films that directly affected by the substrate temperature of the pure and Al doped ZnO thin films.
The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression
Directory of Open Access Journals (Sweden)
Chunxiao Zhang
2012-01-01
Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.
Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J
2011-09-01
When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel.
Alessandri, Angelo; Gaggero, Mauro; Zoppoli, Riccardo
2012-06-01
Optimal control for systems described by partial differential equations is investigated by proposing a methodology to design feedback controllers in approximate form. The approximation stems from constraining the control law to take on a fixed structure, where a finite number of free parameters can be suitably chosen. The original infinite-dimensional optimization problem is then reduced to a mathematical programming one of finite dimension that consists in optimizing the parameters. The solution of such a problem is performed by using sequential quadratic programming. Linear combinations of fixed and parameterized basis functions are used as the structure for the control law, thus giving rise to two different finite-dimensional approximation schemes. The proposed paradigm is general since it allows one to treat problems with distributed and boundary controls within the same approximation framework. It can be applied to systems described by either linear or nonlinear elliptic, parabolic, and hyperbolic equations in arbitrary multidimensional domains. Simulation results obtained in two case studies show the potentials of the proposed approach as compared with dynamic programming.
Parameter specification for the degree distribution of simulated Barabási-Albert graphs
Mohd-Zaid, Fairul; Kabban, Christine M. Schubert; Deckro, Richard F.; White, Edward D.
2017-01-01
The degree distribution of a simulated Barabási-Albert graph under linear preferential attachment is investigated. Specifically, the parameters of the power law distribution are estimated and compared against the theoretical values derived using mean field theory. Least squares method and MLE-nonparametric method were utilized to estimate the distribution parameters on 1000 simulated Barabási-Albert graphs for edge parameter m ∈ { 2 , 4 , 6 } and size n ∈ {2k : k = 5 , 6 , … , 14 , 15 } . Goodness of fit metrics were computed on a second set of simulated graphs for the median of the estimated parameters and other hypothetical values for the distribution parameters. The results suggest that the distribution of the parameters from simulated graphs are significantly different from the theoretical distribution and is also dependent on m. Further results confirm the finding that the parameter of the power law distribution, β, increases as m increases.
Yoo, Sung Jin
2013-04-01
In this brief, we study the distributed consensus tracking control problem for multiple strict-feedback systems with unknown nonlinearities under a directed graph topology. It is assumed that the leader's output is time-varying and has been accessed by only a small fraction of followers in a group. The distributed dynamic surface design approach is proposed to design local consensus controllers in order to guarantee the consensus tracking between the followers and the leader. The function approximation technique using neural networks is employed to compensate unknown nonlinear terms induced from the controller design procedure. From the Lyapunov stability theorem, it is shown that the consensus errors are cooperatively semiglobally uniformly ultimately bounded and converge to an adjustable neighborhood of the origin.
Müller, Markus; Meztler, Holger; Glatt, Anna; Sierra, Carlos
2016-04-01
We present theoretical methods to compute dynamic residence and transit time distributions for non-autonomous systems of pools governed by coupled nonlinear differential equations. Although transit time and age distributions have been used to describe reservoir models for a long time, a closer look to their assumptions reveals two major restrictions of generality in previous studies. First, the systems are assumed to be in equilibrium; and second, the equations under consideration are assumed to be linear. While both these assumptions greatly ease the computation and interpretation of transit time and age distributions they are not applicable to a wide range of problems. Moreover, the transfer of previous results learned from linear systems in steady state to the more complex nonlinear non-autonomous systems that do not even need to have equilibria, can be dangerously misleading. Fortunately the topic of time dependent age and transit time distributions has received some attention recently in hydrology, we aim to compute these distributions for systems of multiple reservoirs. We will discuss how storage selection functions can augment the information represented in an ODE system describing a system of reservoirs. We will present analytical and numerical algorithms and a Monte Carlo simulator to compute solutions for system transit time and age distributions for system-wide storage selection functions including the most simple, but important case of well mixed pools.
Directory of Open Access Journals (Sweden)
Sghaier T
2016-10-01
Full Text Available The objective of this study was to evaluate the effectiveness of both Normal and two-parameter Weibull distributions in describing diameter distribution of Tetraclinis articulata stands in north-east Tunisia. The parameters of the Weibull function were estimated using the moments method and maximum likelihood approaches. The data used in this study came from temporary plots. The three diameter distribution models were compared firstly by estimating the parameters of the distribution directly from individual tree measurements taken in each plot (parameter estimation method, and secondly by predicting the same parameters from stand variables (parameter prediction method. The comparison was based on bias, mean absolute error, mean square error and the Reynolds’ index error (as a percentage. On the basis of the parameter estimation method, the Normal distribution gave slightly better results, whereas the Weibull distribution with the maximum likelihood approach gave the best results for the parameter prediction method. Hence, in the latter case, the Weibull distribution with the maximum likelihood approach appears to be the most suitable to estimate the parameters for reducing the different comparison criteria for the distribution of trees by diameter class in Tetraclinis articulata forests in Tunisia.
Directory of Open Access Journals (Sweden)
Jong-Yun Yoon
2015-08-01
Full Text Available Torsional systems with gear pairs such as the gearbox of wind turbines or vehicle driveline systems inherently show impact phenomena due to clearance-type nonlinearities when the system experiences sinusoidal excitation. This research investigates the vibro-impact energy of unloaded gears in geared systems using the harmonic balance method (HBM in both the frequency and time domains. To achieve accurate simulations, nonlinear models with piecewise and clearance-type nonlinearities and drag torques are defined and implemented in the HBM. Next, the nonlinear frequency responses are examined by focusing on the resonance areas where the impact phenomena occur, along with variations in key parameters such as clutch stiffness, drag torque, and inertias of the flywheel and the unloaded gear. Finally, the effects of the parameters on the vibro-impacts at a specific excitation frequency are explained using bifurcation diagrams. The results are correlated with prior research by defining the gear rattle criteria with key parameters. This article suggests a method to simulate the impact phenomena in torsional systems using the HBM and successfully assesses vibro-impact energy using bifurcation diagrams.
El-Ferik, Sami; Siddiqui, Bilal A.; Frank L. Lewis
2015-01-01
This is an extended version of a technical note accepted for publication in IEEE Transactions on Automatic Control. The note proposes an Input to State practically Stable (ISpS) formulation of distributed nonlinear model predictive controller (NMPC) for formation control of constrained autonomous vehicles in presence of communication bandwidth limitation and transmission delays. Planned trajectories are compressed using neural networks resulting in considerable reduction of data packet size, ...
A dynamic macromodel for distributed parameter magnetic microactuators
Institute of Scientific and Technical Information of China (English)
Fang Yu-Ming; Huang Qing-An; Li Wei-Hua
2008-01-01
This paper presents a reduced-order model to describe the mechanical behaviour of microbeam-based magnetic devices.The integration for magnetic force is calculated by dividing the microbeam into several segments,and the nonlinear equation set has been developed based on the magnetic circuit principle.In comparison with previous models,the present macromodel accounts for both the micro-magnetic-core reluctance and the coupling between the beam deflection and magnetic force.This mazromodel is validated by comparing with the experimental results available in some papers and finite-element solutions.
Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang
2016-07-21
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.
Directory of Open Access Journals (Sweden)
Bin Wang
2016-01-01
Full Text Available This paper studies the application of frequency distributed model for finite time control of a fractional order nonlinear hydroturbine governing system (HGS. Firstly, the mathematical model of HGS with external random disturbances is introduced. Secondly, a novel terminal sliding surface is proposed and its stability to origin is proved based on the frequency distributed model and Lyapunov stability theory. Furthermore, based on finite time stability and sliding mode control theory, a robust control law to ensure the occurrence of the sliding motion in a finite time is designed for stabilization of the fractional order HGS. Finally, simulation results show the effectiveness and robustness of the proposed scheme.
Estimating the Parameters of the Beta-Binomial Distribution.
Wilcox, Rand R.
1979-01-01
For some situations the beta-binomial distribution might be used to describe the marginal distribution of test scores for a particular population of examinees. Several different methods of approximating the maximum likelihood estimate were investigated, and it was found that the Newton-Raphson method should be used when it yields admissable…
An Iterated Local Search Algorithm for Estimating the Parameters of the Gamma/Gompertz Distribution
Directory of Open Access Journals (Sweden)
Behrouz Afshar-Nadjafi
2014-01-01
Full Text Available Extensive research has been devoted to the estimation of the parameters of frequently used distributions. However, little attention has been paid to estimation of parameters of Gamma/Gompertz distribution, which is often encountered in customer lifetime and mortality risks distribution literature. This distribution has three parameters. In this paper, we proposed an algorithm for estimating the parameters of Gamma/Gompertz distribution based on maximum likelihood estimation method. Iterated local search (ILS is proposed to maximize likelihood function. Finally, the proposed approach is computationally tested using some numerical examples and results are analyzed.
Target tracking by distributed autonomous vessels using the derivative-free nonlinear Kalman filter
Rigatos, Gerasimos; Siano, Pierluigi; Raffo, Guilerme
2015-12-01
In this paper a distributed control problem for unmanned surface vessels (USVs) is formulated as follows: there are N USVs which pursue another vessel (moving target). At each time instant each USV can obtain measurements of the target's cartesian coordinates. The objective is to make the USVs converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in their motion plane. A distributed control law is developed for the USVs which enables not only convergence of the USVs to the goal position, but also makes possible to maintain the cohesion of the USVs fleet. Moreover, distributed filtering is performed, so as to obtain an estimate of the target vessel's state vector. This provides the desirable state vector to be tracked by each one of the USVs. To this end, a new distributed nonlinear filtering method of improved accuracy and computation speed is introduced. This filtering approach, under the name Derivative-free distributed nonlinear Kalman Filter is based on differential flatness theory and on an exact linearization of the target vessel's dynamic/kinematic model.
Suresha, Suhas; Sujith, R. I.; Emerson, Benjamin; Lieuwen, Tim
2016-10-01
The flame or flow behavior of a turbulent reacting wake is known to be fundamentally different at high and low values of flame density ratio (ρu/ρb ), as the flow transitions from globally stable to unstable. This paper analyzes the nonlinear dynamics present in a bluff-body stabilized flame, and identifies the transition characteristics in the wake as ρu/ρb is varied over a Reynolds number (based on the bluff-body lip velocity) range of 1000-3300. Recurrence quantification analysis (RQA) of the experimentally obtained time series of the flame edge fluctuations reveals that the time series is highly aperiodic at high values of ρu/ρb and transitions to increasingly correlated or nearly periodic behavior at low values. From the RQA of the transverse velocity time series, we observe that periodicity in the flame oscillations are related to periodicity in the flow. Therefore, we hypothesize that this transition from aperiodic to nearly periodic behavior in the flame edge time series is a manifestation of the transition in the flow from globally stable, convective instability to global instability as ρu/ρb decreases. The recurrence analysis further reveals that the transition in periodicity is not a sudden shift; rather it occurs through an intermittent regime present at low and intermediate ρu/ρb . During intermittency, the flow behavior switches between aperiodic oscillations, reminiscent of a globally stable, convective instability, and periodic oscillations, reminiscent of a global instability. Analysis of the distribution of the lengths of the periodic regions in the intermittent time series and the first return map indicate the presence of type-II intermittency.
Variations in epidemic distribution with some characteristic parameters
Institute of Scientific and Technical Information of China (English)
Liu Zhen-Zhen; Wang Xing-Yuan; Wang Mao-Ji
2012-01-01
Considering the spread of an epidemic among a population of mobile agents that can get infected and maintain the infection for a period,we investigate the variation in the homogeneity of the distribution of the epidemic with the remaining time of infection τ,the velocity modulus of the agent υ,and the infection rate α.We find that the distribution of the infected cluster size is always exponential.By analyzing the variation of the characteristic infected cluster size coefficient,we show that the inhomogeneity of epidemic distribution increases with an increase in τ for very low υ,while it decreases with an increase in τ for moderate υ.The epidemic distribution also tends to a homogeneous state as both υ and α increase.
Simulated Galactic methanol maser distribution to constrain Milky Way parameters
Quiroga-Nuñez, L. H.; van Langevelde, H. J.; Reid, M. J.; Green, J. A.
2017-08-01
Context. Using trigonometric parallaxes and proper motions of masers associated with massive young stars, the Bar and Spiral Structure Legacy (BeSSeL) survey has reported the most accurate values of the Galactic parameters so far. The determination of these parameters with high accuracy has a widespread impact on Galactic and extragalactic measurements. Aims: This research is aimed at establishing the confidence with which such parameters can be determined. This is relevant for the data published in the context of the BeSSeL survey collaboration, but also for future observations, in particular from the southern hemisphere. In addition, some astrophysical properties of the masers can be constrained, notably the luminosity function. Methods: We have simulated the population of maser-bearing young stars associated with Galactic spiral structure, generating several samples and comparing them with the observed samples used in the BeSSeL survey. Consequently, we checked the determination of Galactic parameters for observational biases introduced by the sample selection. Results: Galactic parameters obtained by the BeSSeL survey do not seem to be biased by the sample selection used. In fact, the published error estimates appear to be conservative for most of the parameters. We show that future BeSSeL data and future observations with southern arrays will improve the Galactic parameters estimates and smoothly reduce their mutual correlation. Moreover, by modeling future parallax data with larger distance values and, thus, greater relative uncertainties for a larger numbers of sources, we found that parallax-distance biasing is an important issue. Hence, using fractional parallax uncertainty in the weighting of the motion data is imperative. Finally, the luminosity function for 6.7 GHz methanol masers was determined, allowing us to estimate the number of Galactic methanol masers.
Development of distributed time-variant gain model for nonlinear hydrological systems
Institute of Scientific and Technical Information of China (English)
XIA; Jun; WANG; Gangsheng; TAN; Ge; YE; Aizhong; G.; H.; Hua
2005-01-01
In this paper, a rainfall-runoff modeling system is developed based on a nonlinear Volterra functional series and a hydrological conceptual modeling approach. Two models, i.e. the time-variant gain model (TVGM) and the distributed time-variant gain model (DTVGM) that are built on the platform of Digital Elevation Model (DEM), Remote Sensing (RS) and Unit Hydrological Process were proposed. The developed DTVGM model was applied to two cases in the Heihe River Basin that is located in the arid and semiarid region of northwestern China and the Chaobai River basin located in the semihumid region of northern China. The results indicate that, in addition to the classic dynamic differential approach to describe nonlinear processes in hydrological systems, it is possible to study such complex processes through the proposed systematic approach to identify prominent hydrological relations. The DTVGM, coupling the advantages of both nonlinear and distributed hydrological models, can simulate variant hydrological processes under different environment conditions. Satisfactory results were obtained in forecasting the time-space variations of hydrological processes and the relationships between land use/cover change and surface runoff variation.
Eleiwi, Fadi
2015-07-01
This paper presents a nonlinear Lyapunov-based boundary control for the temperature difference of a membrane distillation boundary layers. The heat transfer mechanisms inside the process are modeled with a 2D advection-diffusion equation. The model is semi-descretized in space, and a nonlinear state-space representation is provided. The control is designed to force the temperature difference along the membrane sides to track a desired reference asymptotically, and hence a desired flux would be generated. Certain constraints are put on the control law inputs to be within an economic range of energy supplies. The effect of the controller gain is discussed. Simulations with real process parameters for the model, and the controller are provided. © 2015 American Automatic Control Council.
Modeling of non-linear CHP efficiency curves in distributed energy systems
DEFF Research Database (Denmark)
Milan, Christian; Stadler, Michael; Cardoso, Gonçalo
2015-01-01
Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation...... for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation......, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two...
Indian Academy of Sciences (India)
Tarsem Singh Gill; Harvinder Kaur
2000-11-01
The effects of nonthermal ion distribution and ﬁnite dust temperature are incorporated in the investigation of nonlinear dust acoustic waves in an unmagnetized dusty plasma. Sagdeev pseudopotential method which takes into account the full nonlinearity of plasma equations, is used here to study solitary wave solutions. Possibility of co-existence of refractive and compressive solitons as a function of Mach number, dust temperature and concentration of nonthermal ions, is considered. For the ﬁxed value of nonthermal ions, it is found that the effect of increase in dust temperature is to reduce the range of co-existence of compressive and refractive solitons. Particular concentration of nonthermal ions results in disappearance of refractive solitons while the decrease in dust temperature, at this concentration restores the lost refractive solitons.
Distributed Lag Linear and Non-Linear Models in R: The Package dlnm
Directory of Open Access Journals (Sweden)
Antonio Gasparrini
2011-08-01
Full Text Available Distributed lag non-linear models (DLNMs represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.
Shape control of distributed parameter reflectors using sliding mode control
Andoh, Fukashi; Washington, Gregory N.; Utkin, Vadim
2001-08-01
Sliding mode control has become one of the most powerful control methods for variable structure systems, a set of continuous systems with an appropriate switching logic. Its robustness properties and order reduction capability have made sliding mode control one of the most efficient tools for relatively higher order nonlinear plants operating under uncertain conditions. Piezo-electric materials possess the property of creating a charge when subjected to a mechanical strain, and of generating a strain when subjected to an electric field. Piezo-electric actuators are known to have a hysteresis due to the thermal motion and Coulomb interaction of Weiss domains. Because of the thermal effect the hysteresis of piezo-electric actuators is reproducible only with some uncertainty in experiments. The robustness of sliding mode control under uncertain conditions has an advantage in handling the hysteresis of piezo-electric actuators. In this research sliding mode control is used to control the shape of one- and two-dimensionally curved adaptive reflectors with piezo-electric actuators. Four discrete linear actuators for the one-dimensionally curved reflector and eight actuators for the two-dimensionally curved reflector are assumed.
Muraglia, Magali; Yagi, Masatoshi; Benkadda, Sadruddin; Peter, Beyer; Garbet, Xavier; Itoh, Sanae -I; Itoh, Kimitaka; Sen, Abhijit
2011-01-01
We present numerical simulation studies of 2D reduced MHD equations investigating the impact of the electronic \\beta parameter and of curvature effects on the nonlinear evolution of drift tearing islands. We observe a bifurcation phenomenon that leads to an amplification of the pressure energy, the generation of E \\times B poloidal flow and a nonlinear diamagnetic drift that affects the rotation of the magnetic island. These dynamical modifications arise due to quasilinear effects that generate a zonal flow at the onset point of the bifurcation. Our simulations show that the transition point is influenced by the \\beta parameter such that the pressure gradient through a curvature effect strongly stabilizes the transition. Regarding the modified rotation of the island, a model for the frequency is derived in order to study its origin and the effect of the \\beta parameter. It appears that after the transition, an E \\times B poloidal flow as well as a nonlinear diamagnetic drift are generated due to an amplificat...
Sopuerta, C F; Gualtieri, L; Sopuerta, Carlos F.; Bruni, Marco; Gualtieri, Leonardo
2003-01-01
We present a new way of deriving gauge transformations in non--linear relativistic perturbation theory. The main ingredient in this formulation is the use of the Baker-Campbell-Hausdorff formula. The associated formal machinery allows us to generalize one-parameter perturbation theory to an arbitrary number of parameters, and to prove the main results concerning the consistency of the scheme to any order in the perturbations. Gauge transformations at any required order can then be directly derived from a generating exponential formula via a simple Taylor expansion. We outline the relation between our novel formulation and previous results.
Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.
Shen, Qikun; Shi, Peng; Shi, Yan
2016-12-01
In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.
Huang, Z; Huang, Zheng; Suzuki, Mahiko
1996-01-01
We obtain the general analytic solutions of the nonlinear \\sigma-model in 3+1 dimensions as the candidates for the disoriented chiral condensate (DCC). The nonuniformly isospin-orientated solutions are shown to be related to the uniformly oriented ones through the chiral (axial) rotations. We discuss the pion charge distribution arising from these solutions. The distribution dP/df=1/(2\\sqrt{f}) holds for the uniform solutions in general and the nonuniform solutions in the 1+1 boost invariant case. For the nonuniform solution in 1+1 without a boost-invariance and in higher dimensions, the distribution does not hold in the integrated form. However, it is applicable to the pions selected from a small segment in the momentum phase space. We suggest that the nonuniform DCC's may correspond to the mini-Centauro events.
Improving Distribution Resiliency with Microgrids and State and Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Tuffner, Francis K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Williams, Tess L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Schneider, Kevin P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Elizondo, Marcelo A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sun, Yannan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Liu, Chen-Ching [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Xu, Yin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Gourisetti, Sri Nikhil Gup [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2015-09-30
Modern society relies on low-cost reliable electrical power, both to maintain industry, as well as provide basic social services to the populace. When major disturbances occur, such as Hurricane Katrina or Hurricane Sandy, the nation’s electrical infrastructure can experience significant outages. To help prevent the spread of these outages, as well as facilitating faster restoration after an outage, various aspects of improving the resiliency of the power system are needed. Two such approaches are breaking the system into smaller microgrid sections, and to have improved insight into the operations to detect failures or mis-operations before they become critical. Breaking the system into smaller sections of microgrid islands, power can be maintained in smaller areas where distribution generation and energy storage resources are still available, but bulk power generation is no longer connected. Additionally, microgrid systems can maintain service to local pockets of customers when there has been extensive damage to the local distribution system. However, microgrids are grid connected a majority of the time and implementing and operating a microgrid is much different than when islanded. This report discusses work conducted by the Pacific Northwest National Laboratory that developed improvements for simulation tools to capture the characteristics of microgrids and how they can be used to develop new operational strategies. These operational strategies reduce the cost of microgrid operation and increase the reliability and resilience of the nation’s electricity infrastructure. In addition to the ability to break the system into microgrids, improved observability into the state of the distribution grid can make the power system more resilient. State estimation on the transmission system already provides great insight into grid operations and detecting abnormal conditions by leveraging existing measurements. These transmission-level approaches are expanded to using
DEFF Research Database (Denmark)
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving...... average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre...... function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes...
Modeling and Control in Distributed Parameter Physical Systems.
2007-11-02
describe the transport of solutes within the liver. Our particular interest is the chemical compound 2,3,7,8-tetrachloroclibenzo-p-dioxin ( TCDD ). TCDD ...herbicides. In particular, TCDD is an unwanted by- product in the manufacture of 2,4,5-trichlorophenoxyacetic acid (2,4,5-T) which was a primary component...describe the hep- atic uptake, distribution, and elimination of TCDD have generally used the well- stirred or venous-equilibrium model to describe
On Parameters Estimation of Lomax Distribution under General Progressive Censoring
Directory of Open Access Journals (Sweden)
Bander Al-Zahrani
2013-01-01
Full Text Available We consider the estimation problem of the probability S=P(Y
Wang, Jun-Wei; Wu, Huai-Ning; Li, Han-Xiong
2012-06-01
In this paper, a distributed fuzzy control design based on Proportional-spatial Derivative (P-sD) is proposed for the exponential stabilization of a class of nonlinear spatially distributed systems described by parabolic partial differential equations (PDEs). Initially, a Takagi-Sugeno (T-S) fuzzy parabolic PDE model is proposed to accurately represent the nonlinear parabolic PDE system. Then, based on the T-S fuzzy PDE model, a novel distributed fuzzy P-sD state feedback controller is developed by combining the PDE theory and the Lyapunov technique, such that the closed-loop PDE system is exponentially stable with a given decay rate. The sufficient condition on the existence of an exponentially stabilizing fuzzy controller is given in terms of a set of spatial differential linear matrix inequalities (SDLMIs). A recursive algorithm based on the finite-difference approximation and the linear matrix inequality (LMI) techniques is also provided to solve these SDLMIs. Finally, the developed design methodology is successfully applied to the feedback control of the Fitz-Hugh-Nagumo equation.
Current distribution effects in patterned non-linear magnetoresistive tunnel junctions
Montaigne, F; Schuhl, A
2000-01-01
To be used in submicronic devices like magnetic memories, magnetic tunnel junctions require low resistances. Four-probe measurements of such resistances are often altered by non-uniformity of the current distribution in the junction. The measured resistance is decreased by localised preferential conduction and increased by voltage drop in the measure electrode. Competition between these two effects is investigated as a function of the geometry. The non-linear conduction of tunnel junctions amplifies dramatically these phenomena and can modify by more than 50% the measured resistance.
Institute of Scientific and Technical Information of China (English)
孙孚; 丁平兴
1994-01-01
Based upon the nonlinear model of random sea waves,a commonly applicable statisticaldistribution of wave surface elevation exact to the third order is derived through the direct calculations ofeach order moment.The distribution arrived reduces,in the sense of being exact to H6,to the Gram-Charlierseries due to Longuet-Higgins for deep water,provided that only the two simplest kinds of wave-wave inter-actions are taken into account The reason why the agreement of Gram-Charlier series with experimental databecomes worse and worse as the terms of series are increased is explicited for the first time.
Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions.
Lachos, Victor H; Bandyopadhyay, Dipankar; Garay, Aldo M
2011-08-01
An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing maximum likelihood (ML) estimates and the observed information matrix is derived analytically. Simulation studies demonstrate the robustness of this flexible class against outlying and influential observations, as well as nice asymptotic properties of the proposed EM-type ML estimates. Finally, the methodology is illustrated using an ultrasonic calibration data.
Directory of Open Access Journals (Sweden)
S. Vaidyanathan
2013-09-01
Full Text Available This research work describes the modelling of two novel 3-D chaotic systems, the first with a hyperbolic sinusoidal nonlinearity and two quadratic nonlinearities (denoted as system (A and the second with a hyperbolic cosinusoidal nonlinearity and two quadratic nonlinearities (denoted as system (B. In this work, a detailed qualitative analysis of the novel chaotic systems (A and (B has been presented, and the Lyapunov exponents and Kaplan-Yorke dimension of these chaotic systems have been obtained. It is found that the maximal Lyapunov exponent (MLE for the novel chaotic systems (A and (B has a large value, viz. for the system (A and for the system (B. Thus, both the novel chaotic systems (A and (B display strong chaotic behaviour. This research work also discusses the problem of finding adaptive controllers for the global chaos synchronization of identical chaotic systems (A, identical chaotic systems (B and nonidentical chaotic systems (A and (B with unknown system parameters. The adaptive controllers for achieving global chaos synchronization of the novel chaotic systems (A and (B have been derived using adaptive control theory and Lyapunov stability theory. MATLAB simulations have been shown to illustrate the novel chaotic systems (A and (B, and also the adaptive synchronization results derived for the novel chaotic systems (A and (B.
Shape Distributions of Nonlinear Dynamical Systems for Video-Based Inference.
Venkataraman, Vinay; Turaga, Pavan
2016-12-01
This paper presents a shape-theoretic framework for dynamical analysis of nonlinear dynamical systems which appear frequently in several video-based inference tasks. Traditional approaches to dynamical modeling have included linear and nonlinear methods with their respective drawbacks. A novel approach we propose is the use of descriptors of the shape of the dynamical attractor as a feature representation of nature of dynamics. The proposed framework has two main advantages over traditional approaches: a) representation of the dynamical system is derived directly from the observational data, without any inherent assumptions, and b) the proposed features show stability under different time-series lengths where traditional dynamical invariants fail. We illustrate our idea using nonlinear dynamical models such as Lorenz and Rossler systems, where our feature representations (shape distribution) support our hypothesis that the local shape of the reconstructed phase space can be used as a discriminative feature. Our experimental analyses on these models also indicate that the proposed framework show stability for different time-series lengths, which is useful when the available number of samples are small/variable. The specific applications of interest in this paper are: 1) activity recognition using motion capture and RGBD sensors, 2) activity quality assessment for applications in stroke rehabilitation, and 3) dynamical scene classification. We provide experimental validation through action and gesture recognition experiments on motion capture and Kinect datasets. In all these scenarios, we show experimental evidence of the favorable properties of the proposed representation.
Luo, Biao; Wu, Huai-Ning; Li, Han-Xiong
2015-04-01
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness.
Directory of Open Access Journals (Sweden)
Rania, M. Shalaby
2015-10-01
Full Text Available This paper deals with Bayesian and non-Bayesian methods for estimating parameters of the bivariate Pareto (BP distribution based on censored samples are considered with shape parameters λ and known scale parameter β. The maximum likelihood estimators MLE of the unknown parameters are derived. The Bayes estimators are obtained with respect to the squared error loss function and the prior distributions allow for prior dependence among the components of the parameter vector. .Posterior distributions for parameters of interest are derived and their properties are described. If the scale parameter is known, the Bayes estimators of the unknown parameters can be obtained in explicit forms under the assumptions of independent priors. An extensive computer simulation is used to compare the performance of the proposed estimators using MathCAD (14.
Regularization method with two parameters for nonlinear ill-posed problems
Institute of Scientific and Technical Information of China (English)
2008-01-01
This paper is devoted to the regularization of a class of nonlinear ill-posed problems in Banach spaces. The operators involved are multi-valued and the data are assumed to be known approximately. Under the assumption that the original problem is solvable, a strongly convergent approximation procedure is designed by means of the Tikhonov regularization method with two pa- rameters.
On Non-Linear Sensitivity of Marine Biological Models to Parameter Variations
2007-01-01
M.B., 2002. Understanding uncertain enviromental systems. In: Grasman, J., van Straten, G. (Eds.), Predictability and Nonlinear Modelling in Natural...Lekien, F., 2006. Quantifying uncertainities in ocean predictions. In: Paluszkiewicz, T., Harper, S. (Eds.), Oceanography, special issue on Advances in
Azoug, Seif Eddine; Bouguezel, Saad
2016-01-01
In this paper, a novel opto-digital image encryption technique is proposed by introducing a new non-linear preprocessing and using the multiple-parameter discrete fractional Fourier transform (MPDFrFT). The non-linear preprocessing is performed digitally on the input image in the spatial domain using a piecewise linear chaotic map (PLCM) coupled with the bitwise exclusive OR (XOR). The resulting image is multiplied by a random phase mask before applying the MPDFrFT to whiten the image. Then, a chaotic permutation is performed on the output of the MPDFrFT using another PLCM different from the one used in the spatial domain. Finally, another MPDFrFT is applied to obtain the encrypted image. The parameters of the PLCMs together with the multiple fractional orders of the MPDFrFTs constitute the secret key for the proposed cryptosystem. Computer simulation results and security analysis are presented to show the robustness of the proposed opto-digital image encryption technique and the great importance of the new non-linear preprocessing introduced to enhance the security of the cryptosystem and overcome the problem of linearity encountered in the existing permutation-based opto-digital image encryption schemes.
Quach, Minh; Brunel, Nicolas; d'Alché-Buc, Florence
2007-12-01
Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. Matlab code available on demand.
Directory of Open Access Journals (Sweden)
G. Sun
2011-11-01
Full Text Available Human activities and climate change are important factors that affect grassland ecosystems. A new optimization approach, the approach of conditional nonlinear optimal perturbation (CNOP related to initial and parameter perturbations, is employed to explore the nonlinearly combined impacts of human activities and climate change on a grassland ecosystem using a theoretical grassland model. In our study, it is assumed that the initial perturbations and parameter perturbations are regarded as human activities and climate change, respectively. Numerical results indicate that the climate changes causing the maximum effect in the grassland ecosystem are different under disparate intensities of human activities. This implies the pattern of climate change is very critical to the maintenance or degradation of grassland ecosystem in light of high intensity of human activities and that the grassland ecosystem should be rationally managed when the moisture index decreases. The grassland ecosystem influenced by the nonlinear combination of human activities and climate change undergoes abrupt change, while the grassland ecosystem affected by other types of human activities and climate change fails to show the abrupt change under a certain range of perturbations with the theoretical model. The further numerical analyses also indicate that the growth of living biomass and the evaporation from soil surface shaded by the wilted biomass may be crucial factors contributing to the abrupt change of the grassland equilibrium state within the theoretical model.
Simulation of a class of delay stochastic system with distributed parameter
Institute of Scientific and Technical Information of China (English)
Song Yanan; Deng Feiqi; Luo Qi
2005-01-01
Simulation of a class of delay stochastic system with distributed parameter is discussed. Difference schemes for the numerical computation of delay stochastic system are obtained. The precision of the difference scheme and the efficiency of the difference scheme in simulation of delay stochastic system with distributed parameter are analyzed. Examples are given to illustrate the application of the method.
Modelling and controller design for distributed parameter systems via residence time distribution
Mourik, van S.; Zwart, H.; Keesman, K.J.
2009-01-01
For chemical reactors with non-linear fluid dynamics, a linear model realisation is proposed. The inputs are the ingoing concentration of a certain component in the fluid, and the reaction rate. The output is the outgoing concentration. The realisation makes use of a first-order reaction equation, a
Asymptotic Results for the Two-parameter Poisson-Dirichlet Distribution
Feng, Shui
2009-01-01
The two-parameter Poisson-Dirichlet distribution is the law of a sequence of decreasing nonnegative random variables with total sum one. It can be constructed from stable and Gamma subordinators with the two-parameters, $\\alpha$ and $\\theta$, corresponding to the stable component and Gamma component respectively. The moderate deviation principles are established for the two-parameter Poisson-Dirichlet distribution and the corresponding homozygosity when $\\theta$ approaches infinity, and the large deviation principle is established for the two-parameter Poisson-Dirichlet distribution when both $\\alpha$ and $\\theta$ approach zero.
Eleiwi, Fadi
2016-09-19
This paper presents a nonlinear observer-based Lyapunov control for a membrane distillation (MD) process. The control considers the inlet temperatures of the feed and the permeate solutions as inputs, transforming it to boundary control process, and seeks to maintain the temperature difference along the membrane boundaries around a sufficient level to promote water production. MD process is modeled with advection diffusion equation model in two dimensions, where the diffusion and convection heat transfer mechanisms are best described. Model analysis, effective order reduction and parameters physical interpretation, are provided. Moreover, a nonlinear observer has been designed to provide the control with estimates of the temperature evolution at each time instant. In addition, physical constraints are imposed on the control to have an acceptable range of feasible inputs, and consequently, better energy consumption. Numerical simulations for the complete process with real membrane parameter values are provided, in addition to detailed explanations for the role of the controller and the observer. (C) 2016 Elsevier Ltd. All rights reserved.
Beyer's non-linearity parameter (B/A) in benzylidene aniline Schiff base liquid crystalline systems
Energy Technology Data Exchange (ETDEWEB)
Nagi Reddy, M.V.V. [Department of Physics, The Hindu College, Machilipatnam 521 001 (India); Pisipati, V.G.K.M., E-mail: venkata_pisipati@hotmail.co [Liquid Crystal Research Centre, Department of Electronics and Communication Engineering, Koneru Lakshmaiah University, Vaddeswaram 522 502 (India); Madhavi Latha, D. [Liquid Crystal Research Centre, Department of Electronics and Communication Engineering, Koneru Lakshmaiah University, Vaddeswaram 522 502 (India); Datta Prasad, P.V. [Department of Physics, The Hindu College, Machilipatnam 521 001 (India)
2011-02-15
The non-linearity parameter B/A is estimated for a number of liquid crystal materials of the type N-(p-n-alkoxy benzylidene)-p-n-alkyl anilines, popularly known as nO.m, where n and m are the aliphatic chains on either side of the rigid core, which can be varied from 1 to 18 to realize a number of LC materials with a variety LC phase variants. The B/A values are computed from both density and sound velocity data following standard relations reported in literature. This systematic study in a homologous series provides an opportunity to study how this parameter behaves with (1) either the alkoxy and/or alkyl chain number, (2) with the total chain number (n+m), (3) with increase in molecular weight and (4) whether the linear relations reported in literature either with {alpha}T [thermal expansion coefficient ({alpha}) and temperature (T)] and sound velocity (u) will hold good or not and if so to what extent. The results are discussed with the body of data available in literature on liquids, liquid mixtures and other LC materials. -- Research highlights: {yields} The Bayer's non-linearity parameter (B/A) is estimated for the first time for a number liquid crystal materials of the type N-(p-n-alkoxy benzylidene)-p-nalkyl anilines. {yields} The magnitude of B/A estimated from sound velocity data is higher compared to that estimated thermal expansion data. {yields} The B/A value decreases with increase in molecular weight with an even odd fashion and reaches a minimum value and saturates. {yields} These studies reveal that both the thermal expansion coefficient and sound velocity are the tools to estimate the non-linear parameter B/A in the case of liquid crystals.
Abate, Alexandra; Bridle, Sarah; Teodoro, Luis F. A.; Warren, Michael S.; Hendry, Martin
2008-10-01
We investigate methods to best estimate the normalization of the mass density fluctuation power spectrum (σ8) using peculiar velocity data from a survey like the six-degree Field Galaxy Velocity Survey (6dFGSv). We focus on two potential problems: (i) biases from non-linear growth of structure and (ii) the large number of velocities in the survey. Simulations of ΛCDM-like models are used to test the methods. We calculate the likelihood from a full covariance matrix of velocities averaged in grid cells. This simultaneously reduces the number of data points and smoothes out non-linearities which tend to dominate on small scales. We show how the averaging can be taken into account in the predictions in a practical way, and show the effect of the choice of cell size. We find that a cell size can be chosen that significantly reduces the non-linearities without significantly increasing the error bars on cosmological parameters. We compare our results with those from a principal components analysis following Watkins et al. and Feldman et al. to select a set of optimal moments constructed from linear combinations of the peculiar velocities that are least sensitive to the non-linear scales. We conclude that averaging in grid cells performs equally well. We find that for a survey such as 6dFGSv we can estimate σ8 with less than 3 per cent bias from non-linearities. The expected error on σ8 after marginalizing over Ωm is approximately 16 per cent.
Zhao, Meng; Ding, Baocang
2015-03-01
This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable.
A Comparison of Estimation Techniques for the Three Parameter Pareto Distribution
1985-12-01
1897 Vilfredo Pareto (1848-1923), an Italian-born Swiss professor of economics, formulated an empirical law which bears his name (16:233). Pareto’s Law...DTIC00• _ZLECTE! CD S A COMPARISON OF ESTIMATION TECHNIQUES FOR THICTHE REE PARAMETER PARETO DISTRIBUTION THESIS "Dennis J. Charek Major, USAF AFIT...TECHNIQUES FOR THE THREE PARAMETER PARETO DISTRIBUTION THESIS Dennis J. Charek Major, USAF AFIT/GSO/MA/8SD-3 Approved for public release; distribution
SAMDIST: A Computer Code for Calculating Statistical Distributions for R-Matrix Resonance Parameters
Energy Technology Data Exchange (ETDEWEB)
Leal, L.C.
1995-01-01
The: SAMDIST computer code has been developed to calculate distribution of resonance parameters of the Reich-Moore R-matrix type. The program assumes the parameters are in the format compatible with that of the multilevel R-matrix code SAMMY. SAMDIST calculates the energy-level spacing distribution, the resonance width distribution, and the long-range correlation of the energy levels. Results of these calculations are presented in both graphic and tabular forms.
SAMDIST A Computer Code for Calculating Statistical Distributions for R-Matrix Resonance Parameters
Leal, L C
1995-01-01
The: SAMDIST computer code has been developed to calculate distribution of resonance parameters of the Reich-Moore R-matrix type. The program assumes the parameters are in the format compatible with that of the multilevel R-matrix code SAMMY. SAMDIST calculates the energy-level spacing distribution, the resonance width distribution, and the long-range correlation of the energy levels. Results of these calculations are presented in both graphic and tabular forms.
Barbosa, Tiago M; Goh, Wan Xiu; Morais, Jorge E; Costa, Mário J
2016-08-19
The aim was to examine the variation of linear and nonlinear proprieties of the behaviour in participants with different levels of swimming expertise among the four swim strokes. Seventy-five swimmers were split into three groups (highly qualified experts, experts and non-experts) and performed a maximal 25m trial for each of the four competitive swim strokes. A speed-meter cable was attached to the swimmer's hip to measure hip speed; from which speed fluctuation (dv), approximate entropy (ApEn) and fractal dimension (D) variables were derived. Although simple main effects of expertise and swim stroke were obtained for dv and D, no significant interaction of expertise and stroke were found except in ApEn. The ApEn and D were prone to decrease with increasing expertise. As a conclusion, swimming does exhibit nonlinear properties but its magnitude differs according to the swim stroke and level of expertise of the performer.
Higher-dimensional realization of a nonlinear, one-parameter quantum oscillator
Schulze-Halberg, Axel; Morris, John R.
2013-05-01
We generalize a recently introduced quantum model of a nonlinear oscillator to arbitrary dimensions. In our realization of the model we impose hyperspherical symmetry, which allows for separation of variables in the governing equation. We obtain the discrete spectrum in closed form, as well as the corresponding orthogonal set of normalizable eigenfunctions, located in a weighted Hilbert space. Furthermore, conditions for emptiness of the discrete spectrum are obtained, as well as spectral bounds for the eigenvalues.
Word, Daniel P; Cummings, Derek A T; Burke, Donald S; Iamsirithaworn, Sopon; Laird, Carl D
2012-08-07
Mathematical models can enhance our understanding of childhood infectious disease dynamics, but these models depend on appropriate parameter values that are often unknown and must be estimated from disease case data. In this paper, we develop a framework for efficient estimation of childhood infectious disease models with seasonal transmission parameters using continuous differential equations containing model and measurement noise. The problem is formulated using the simultaneous approach where all state variables are discretized, and the discretized differential equations are included as constraints, giving a large-scale algebraic nonlinear programming problem that is solved using a nonlinear primal-dual interior-point solver. The technique is demonstrated using measles case data from three different locations having different school holiday schedules, and our estimates of the seasonality of the transmission parameter show strong correlation to school term holidays. Our approach gives dramatic efficiency gains, showing a 40-400-fold reduction in solution time over other published methods. While our approach has an increased susceptibility to bias over techniques that integrate over the entire unknown state-space, a detailed simulation study shows no evidence of bias. Furthermore, the computational efficiency of our approach allows for investigation of a large model space compared with more computationally intensive approaches.
Lam, H K; Leung, F H F; Lee, Y S
2004-04-01
This paper deals with nonlinear plants subject to unknown parameters. A fuzzy model is first used to represent the plant. An equivalent switching plant model is then derived, which supports the design of a switching controller. It will be shown that the closed-loop system formed by the plant and the switching controller is a linear system. Hence, the system performance of the closed-loop system can be designed. An application example on controlling a two-inverted pendulum system on a cart will be given to illustrate the design procedure of the proposed switching controller.
Synchronization of hyperchaotic Rossler system with uncertain parameters via nonlinear control
Institute of Scientific and Technical Information of China (English)
DONG En-zeng; CHEN Zeng-qiang; YUAN Zhu-zhi
2006-01-01
@@ Based on the Lyapunov stability theory,a new method for synchronization of hyperchaotic Rossler system with uncertain parameters is proposed.By this method,choosing appropriate control law and adaptive update law of uncertain parameters,all the errors of system variable synchronization and of uncertain parameter track are asymptotically stable.The theoretical analysis and the numerical simulations prove the effectiveness of the proposed method.
Syvorotka, Ihor I.; Pavlyk, Lyubomyr P.; Ubizskii, Sergii B.; Buryy, Oleg A.; Savytskyy, Hrygoriy V.; Mitina, Nataliya Y.; Zaichenko, Oleksandr S.
2017-04-01
Method of determining of magnetic moment and size from measurements of dependence of the nonlinear magnetic susceptibility upon magnetic field is proposed, substantiated and tested for superparamagnetic nanoparticles (SPNP) of the "magnetic core-polymer shell" type which are widely used in biomedical technologies. The model of the induction response of the SPNP ensemble on the combined action of the magnetic harmonic excitation field and permanent bias field is built, and the analysis of possible ways to determine the magnetic moment and size of the nanoparticles as well as the parameters of the distribution of these variables is performed. Experimental verification of the proposed method was implemented on samples of SPNP with maghemite core in dry form as well as in colloidal systems. The results have been compared with the data obtained by other methods. Advantages of the proposed method are analyzed and discussed, particularly in terms of its suitability for routine express testing of SPNP for biomedical technology.
Directory of Open Access Journals (Sweden)
Farzin Shabani
Full Text Available Using CLIMEX and the Taguchi Method, a process-based niche model was developed to estimate potential distributions of Phoenix dactylifera L. (date palm, an economically important crop in many counties. Development of the model was based on both its native and invasive distribution and validation was carried out in terms of its extensive distribution in Iran. To identify model parameters having greatest influence on distribution of date palm, a sensitivity analysis was carried out. Changes in suitability were established by mapping of regions where the estimated distribution changed with parameter alterations. This facilitated the assessment of certain areas in Iran where parameter modifications impacted the most, particularly in relation to suitable and highly suitable locations. Parameter sensitivities were also evaluated by the calculation of area changes within the suitable and highly suitable categories. The low temperature limit (DV2, high temperature limit (DV3, upper optimal temperature (SM2 and high soil moisture limit (SM3 had the greatest impact on sensitivity, while other parameters showed relatively less sensitivity or were insensitive to change. For an accurate fit in species distribution models, highly sensitive parameters require more extensive research and data collection methods. Results of this study demonstrate a more cost effective method for developing date palm distribution models, an integral element in species management, and may prove useful for streamlining requirements for data collection in potential distribution modeling for other species as well.
Sensitivity analysis of CLIMEX parameters in modelling potential distribution of Lantana camara L.
Directory of Open Access Journals (Sweden)
Subhashni Taylor
Full Text Available A process-based niche model of L. camara L. (lantana, a highly invasive shrub species, was developed to estimate its potential distribution using CLIMEX. Model development was carried out using its native and invasive distribution and validation was carried out with the extensive Australian distribution. A good fit was observed, with 86.7% of herbarium specimens collected in Australia occurring within the suitable and highly suitable categories. A sensitivity analysis was conducted to identify the model parameters that had the most influence on lantana distribution. The changes in suitability were assessed by mapping the regions where the distribution changed with each parameter alteration. This allowed an assessment of where, within Australia, the modification of each parameter was having the most impact, particularly in terms of the suitable and highly suitable locations. The sensitivity of various parameters was also evaluated by calculating the changes in area within the suitable and highly suitable categories. The limiting low temperature (DV0, limiting high temperature (DV3 and limiting low soil moisture (SM0 showed highest sensitivity to change. The other model parameters were relatively insensitive to change. Highly sensitive parameters require extensive research and data collection to be fitted accurately in species distribution models. The results from this study can inform more cost effective development of species distribution models for lantana. Such models form an integral part of the management of invasive species and the results can be used to streamline data collection requirements for potential distribution modelling.
Aricò, Costanza; Lo Re, Carlo
2016-12-01
We extend a recently proposed 2D depth-integrated Finite Volume solver for the nonlinear shallow water equations with non-hydrostatic pressure distribution. The proposed model is aimed at simulating both nonlinear and dispersive shallow water processes. We split the total pressure into its hydrostatic and dynamic components and solve a hydrostatic problem and a non-hydrostatic problem sequentially, in the framework of a fractional time step procedure. The dispersive properties are achieved by incorporating the non-hydrostatic pressure component in the governing equations. The governing equations are the depth-integrated continuity equation and the depth-integrated momentum equations along the x, y and z directions. Unlike the previous non-hydrostatic shallow water solver, in the z momentum equation, we retain both the vertical local and convective acceleration terms. In the former solver, we keep only the local vertical acceleration term. In this paper, we investigate the effects of these convective terms and the possible improvements of the computed solution when these terms are not neglected in the governing equations, especially in strongly nonlinear processes. The presence of the convective terms in the vertical momentum equation leads to a numerical solution procedure, which is quite different from the one of the previous solver, in both the hydrostatic and dynamic steps. We discretize the spatial domain using unstructured triangular meshes satisfying the Generalized Delaunay property. The numerical solver is shock capturing and easily addresses wetting/drying problems, without any additional equation to solve at wet/dry interfaces. We present several numerical applications for challenging flooding processes encountered in practical aspects over irregular topography, including a new set of experiments carried out at the Hydraulics Laboratory of the University of Palermo.
Quadratic nonlinear optical parameters of 7% MgO-doped LiNbO3 crystal
Kulyk, B.; Kapustianyk, V.; Figà, V.; Sahraoui, B.
2016-06-01
Pure and 7% MgO-doped lithium niobate (LiNbO3) single crystals were grown by the Czochralski technique. The shift of optical absorption edge in 7% MgO-doped crystal in direction of shorter wavelength compared to undoped crystal was observed. The second harmonic generation measurements of 7% MgO-doped LiNbO3 crystal were performed at room temperature by means of the rotational Maker fringe technique using Nd:YAG laser generating at 1064 nm in picoseconds regime. Experimentally obtained value of nonlinear optical coefficient d33 for 7% MgO-doped LiNbO3 was found to be less than for undoped crystal but higher than for 5% MgO-doped. I-type phase-matched second harmonic generation was achieved and the value of phase-matched angle was calculated. High quadratic nonlinearity together with tolerance to intensive laser irradiation makes 7% MgO-doped LiNbO3 crystal interesting for application in optoelectronics.
Effect of illumination on linear and nonlinear optical parameters of Ga5Se95 thin films
Zedan, I. T.; El-Nahass, M. M.
2015-09-01
Ga5Se95 films were prepared by using thermal evaporation technique. X-ray showed that the powder samples as well as thin-film samples are crystalline in nature. The optical constants (refractive index n and rad absorption index k) of Ga5Se95 films were calculated using Murmann's exact method. The photoinduced red shift of the optical gap (photodarkening) appeared in Ga5Se95 films after illumination. The indirect energy gap was decreased from 1.75 eV for the as-deposited films to 1.65 eV for illuminated thin films 1 h. The concentration of color centers was calculated by using Gaussian fitting for peaks of the absorption coefficient ( α) and was found to increase from 5.6 × 1024 to 6.2 × 1024 cm-3 with illumination time. The effect of illumination on the nonlinear optical susceptibility ( χ (3)) and nonlinear refractive index ( n 2) is estimated using empirical relations.
Directory of Open Access Journals (Sweden)
Guoguang Wen
2014-01-01
Full Text Available This paper mainly addresses the distributed consensus tracking problem for second-order nonlinear multiagent systems with a specified reference trajectory. The dynamics of each follower consists of two terms: nonlinear inherent dynamics and a simple communication protocol relying only on the position and velocity information of its neighbors. The consensus reference is taken as a virtual leader, whose output is only its position and velocity information that is available to only a subset of a group of followers. To achieve consensus tracking, a class of nonsmooth control protocols is proposed which reply on the relative information among the neighboring agents. Then some corresponding sufficient conditions are derived. It is shown that if the communication graph associated with the virtual leader and followers is connected at each time instant, the consensus can be achieved at least globally exponentially with the proposed protocol. Rigorous proofs are given by using graph theory, matrix theory, and Lyapunov theory. Finally, numerical examples are presented to illustrate the theoretical analysis.
Zhang, Xian-Xia; Jiang, Ye; Li, Han-Xiong; Li, Shao-Yuan
2013-10-01
A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC.
A derivative-free distributed filtering approach for sensorless control of nonlinear systems
Rigatos, Gerasimos G.
2012-09-01
This article examines the problem of sensorless control for nonlinear dynamical systems with the use of derivative-free Extended Information Filtering (EIF). The system is first subject to a linearisation transformation and next state estimation is performed by applying the standard Kalman Filter to the linearised model. At a second level, the standard Information Filter is used to fuse the state estimates obtained from local derivative-free Kalman filters running at the local information processing nodes. This approach has significant advantages because unlike the EIF (i) is not based on local linearisation of the nonlinear dynamics (ii) does not assume truncation of higher order Taylor expansion terms thus preserving the accuracy and robustness of the performed estimation and (iii) does not require the computation of Jacobian matrices. As a case study a robotic manipulator is considered and a cameras network consisting of multiple vision nodes is assumed to provide the visual information to be used in the control loop. A derivative-free implementation of the EIF is used to produce the aggregate state vector of the robot by processing local state estimates coming from the distributed vision nodes. The performance of the considered sensorless control scheme is evaluated through simulation experiments.
Distributed parameter modeling and thermal analysis of a spiral water wall in a supercritical boiler
Directory of Open Access Journals (Sweden)
Zheng Shu
2013-01-01
Full Text Available In this paper, a distributed parameter model for the evaporation system of a supercritical spiral water wall boiler is developed based on a 3-D temperature field. The mathematical method is formulated for predicting the heat flux and the metal-surface temperature. The results show that the influence of the heat flux distribution is more obvious than that of the heat transfer coefficient distribution in the spiral water wall tube, and the peak of the heat transfer coefficient decreases with an increment of supercritical pressure. This distributed parameter model can be used for a 600 MW supercritical-pressure power plant.
Yang, Cheng-Hsiung; Wu, Cheng-Lin
2014-01-01
An adaptive control scheme is developed to study the generalized adaptive chaos synchronization with uncertain chaotic parameters behavior between two identical chaotic dynamic systems. This generalized adaptive chaos synchronization controller is designed based on Lyapunov stability theory and an analytic expression of the adaptive controller with its update laws of uncertain chaotic parameters is shown. The generalized adaptive synchronization with uncertain parameters between two identical new Lorenz-Stenflo systems is taken as three examples to show the effectiveness of the proposed method. The numerical simulations are shown to verify the results.
Roozegar, Mehdi; Mahjoob, Mohammad J.; Ayati, Moosa
2017-05-01
This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton-Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.
Synchronisation of high-order MIMO nonlinear systems using distributed neuro-adaptive control
Ghiti Sarand, Hassan; Karimi, Bahram
2016-07-01
This paper addresses synchronisation problem of high-order multi-input/multi-output (MIMO) multi-agent systems. Each agent has unknown nonlinear dynamics and is subject to uncertain external disturbances. The agents must follow a reference trajectory. An adaptive distributed controller based on relative information of neighbours of each agent is designed to solve the problem for any undirected connected communication topology. A radial basis function neural network is used to represent the controller's unknown structure. Lyapunov stability analysis is employed to guarantee stability of the overall system. By the theoretical analysis, the closed-loop control system is shown to be uniformly ultimately bounded. Finally, simulations are provided to show effectiveness of the proposed control method against uncertainty and disturbances.
Holzwarth, V R
2003-01-01
Observations of magnetically active close binaries with orbital periods of a few days reveal the existence of starspots at preferred longitudes (with respect to the direction of the companion star). We numerically investigate the non-linear dynamics and evolution of magnetic flux tubes in the convection zoneof a fast-rotating component of a close binary system and explore whether the tidal effects are able to generate non-uniformities in the surface distribution of erupting flux tubes. Assuming a synchronised system with a rotation period of two days and consisting of two solar-type components, both the tidal force and the deviation of the stellar structure from spherical shape are considered in lowest-order perturbation theory. The magnetic field is initially stored in the form of toroidal magnetic flux rings within the stably stratified overshoot region beneath the convection zone. Once the field has grown sufficiently strong, instabilities initiate the formation of rising flux loops, which rise through the...
Zuo, Wenjie; Jiang, Daqing
2016-07-01
In this paper, we investigate the dynamics of the stochastic autonomous and non-autonomous predator-prey systems with nonlinear predator harvesting respectively. For the autonomous system, we first give the existence of the global positive solution. Then, in the case of persistence, we prove that there exists a unique stationary distribution and it has ergodicity by constructing a suitable Lyapunov function. The result shows that, the relatively weaker white noise will strengthen the stability of the system, but the stronger white noise will result in the extinction of one or two species. Particularly, for the non-autonomous periodic system, we show that there exists at least one nontrivial positive periodic solution according to the theory of Khasminskii. Finally, numerical simulations illustrate our theoretical results.
Directory of Open Access Journals (Sweden)
Qihong Chen
2014-01-01
Full Text Available This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX, and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.
Chen, Qihong; Long, Rong; Quan, Shuhai; Zhang, Liyan
2014-01-01
This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.
Statistical distribution of surface elevation for the fourth order nonlinear random sea waves
Institute of Scientific and Technical Information of China (English)
管长龙; 孙孚
1997-01-01
Based upon the nonlinear model of random sea waves, the statistical distribution of wave surface elevation exact to the fourth order is derived as the truncated Gram-Charlier series, by calculating directly each order moment. The phenomenon found by Huang et al. that the agreement between observed data and investigated series deteriorates much more when the series is kept to λ8 is explained. The effect of the approximation order on the truncation of series and the determination of coefficients is investigated. For the mth order approximation, the derived series is truncated at H3m-3 with the absence of H3m-4, and the coefficients of H3m-3 and H3m-6 are connected by a simple algebraic relation.
Directory of Open Access Journals (Sweden)
Kexin Jia
2015-01-01
Full Text Available The paper is concerned with the problem of distributed node-to-node consensus of multiagent systems with delayed nonlinear dynamics and communication constraints. A new kind of consensus protocol based only on the intermittent measurements of neighboring agents is proposed to make each follower track the corresponding leader asymptotically. Based on the Lyapunov stability theory and M-matrix theory, some novel and simple criteria are derived for node-to-node consensus of multiagent systems. It is shown that consensus can be reached if the communication time duration is larger than the corresponding threshold value. Finally, a numerical example is provided to demonstrate the effectiveness of the obtained theoretical results.
Energy Technology Data Exchange (ETDEWEB)
Prakash, Deo [School of Computer Science & Engineering, Faculty of Engineering, SMVD University, Kakryal, Katra 182320, J& K (India); Shaaban, E.R., E-mail: esam_ramadan2008@yahoo.com [Physics Department, Faculty of Science, Al-Azhar University, Assiut 71542 (Egypt); Shapaan, M. [Department of Physics, Faculty of Science, Al-Azahar University, Cairo (Egypt); Mohamed, S.H. [Physics Department, Faculty of Science, Sohag University, 82524 Sohag (Egypt); Othman, A.A. [Physics Department, Faculty of Science, Assiut University, Assiut 71516 (Egypt); Verma, K.D., E-mail: kdverma1215868@gmail.com [Material Science Research Laboratory, Department of Physics, S. V. College, Aligarh 202001, U.P. (India)
2016-08-15
Highlights: • Combined experimental and theoretical researches on ZnSe Thin Films. • The film thickness and refractive index were determined using envelope method. • The absorption coefficient and the energy gap were calculated. • Dispersion parameters were determined using Wemple-DiDomenico relation. • The third order susceptibility and nonlinear refractive index were calculated. - Abstract: Zinc selenide (ZnSe) thin films with different thicknesses were evaporated onto glass substrates using the thermal evaporation technique. X-ray diffraction analysis confirmed that both the film and powder have cubic zinc-blende structure. The fundamental optical parameters like absorption coefficient, extinction coefficient and band gap were evaluated in transparent region of transmittance and reflectance spectrum. The optical transition of the films was found to be allowed, where the energy gap increased from 2.576 to 2.702 eV with increasing film thickness. Also, the refractive index value increase with increasing film thickness. The refractive indices evaluated through envelope method were extrapolated by Cauchy dispersion relationship over the whole spectra range. Additionally, the dispersion of refractive index was determined in terms of Wemple-DiDomenico single oscillator model. Third order susceptibility and nonlinear refractive index were determined for different thickness of ZnSe thin films.
Deng, Wei; Wang, Ya
2017-02-01
This paper reports the systematic parameter study of a tristable nonlinear electromagnetic energy harvester for ambient low-frequency vibration. Numerical simulations and experimental investigations are performed on the harvester which consists of a cantilever beam, a tip coil, two tip magnets and two external side magnets. The external side magnets are deployed symmetrically along a concave surface parallel to the trajectory of the cantilever tip with a controllable distance so that the magnetic orientation of the tip magnets are matched with that of the side magnets. Therefore, instead of the ternary position parameters (d, h, α), a binary parameters pair (d0, d) is used to characterize the position of the side magnets and the performance of the energy harvester. The magnetic force and magnetic field on the cantilever tip therefore depend on the relative distance in the tip displacement direction between the tip magnets and side magnets, but is independent of the position of the side magnets on the concave surface. The magnetic force (field)-distance relationship is measured experimentally and curve fitted to obtain explicit expressions, in order to characterize the magnetic force (field) when the side magnets are placed at varied positions along the concave surface. Numerical simulation is, then, performed to predict the electromagnetic voltage output and the bandwidth of the energy harvester. The simulation results coincided with the measured data. Significant broadband response is obtained experimentally and the maximum RMS power output is 40.2 mW at 0.45g of excitation. The proposed structure showcasing the matched magnetic orientation is characterized by the binary parameters pair (d0, d) and the systematic parametric approach could contribute to the design and study of nonlinear broadband energy harvesters.
Recovering the nonlinear density field from the galaxy distribution with a Poisson-Lognormal filter
Kitaura, Francisco S; Metcalf, R Benton
2009-01-01
We present a general expression for a lognormal filter given an arbitrary nonlinear galaxy bias. We derive this filter as the maximum a posteriori solution assuming a lognormal prior distribution for the matter field with a given mean field and modeling the observed galaxy distribution by a Poissonian process. We have performed a three-dimensional implementation of this filter with a very efficient Newton-Krylov inversion scheme. Furthermore, we have tested it with a dark matter N-body simulation assuming a unit galaxy bias relation and compared the results with previous density field estimators like the inverse weighting scheme and Wiener filtering. Our results show good agreement with the underlying dark matter field for overdensities even above delta~1000 which exceeds by one order of magnitude the regime in which the lognormal is expected to be valid. The reason is that for our filter the lognormal assumption enters as a prior distribution function, but the maximum a posteriori solution is also conditione...
Chapman, G.; Kirk, D.
1974-01-01
The parameter identification scheme being used is a differential correction least squares procedure (Gauss-Newton method). The position, orientation, and derivatives of these quantities with respect to the parameters of interest (i.e., sensitivity coefficients) are determined by digital integration of the equations of motion and the parametric differential equations. The application of this technique to three vastly different sets of data is used to illustrate the versatility of the method and to indicate some of the problems that still remain.
Nonlinear saturation of wave packets excited by low-energy electron horseshoe distributions.
Krafft, C; Volokitin, A
2013-05-01
Horseshoe distributions are shell-like particle distributions that can arise in space and laboratory plasmas when particle beams propagate into increasing magnetic fields. The present paper studies the stability and the dynamics of wave packets interacting resonantly with electrons presenting low-energy horseshoe or shell-type velocity distributions in a magnetized plasma. The linear instability growth rates are determined as a function of the ratio of the plasma to the cyclotron frequencies, of the velocity and the opening angle of the horseshoe, and of the relative thickness of the shell. The nonlinear stage of the instability is investigated numerically using a symplectic code based on a three-dimensional Hamiltonian model. Simulation results show that the dynamics of the system is mainly governed by wave-particle interactions at Landau and normal cyclotron resonances and that the high-order normal cyclotron resonances play an essential role. Specific features of the dynamics of particles interacting simultaneously with two or more waves at resonances of different natures and orders are discussed, showing that such complex processes determine the main characteristics of the wave spectrum's evolution. Simulations with wave packets presenting quasicontinuous spectra provide a full picture of the relaxation of the horseshoe distribution, revealing two main phases of the evolution: an initial stage of wave energy growth, characterized by a fast filling of the shell, and a second phase of slow damping of the wave energy, accompanied by final adjustments of the electron distribution. The influence of the density inhomogeneity along the horseshoe on the wave-particle dynamics is also discussed.
Directory of Open Access Journals (Sweden)
Mohamed Mahmoud Mohamed
2016-09-01
Full Text Available In this paper we develop approximate Bayes estimators of the parameters,reliability, and hazard rate functions of the Logistic distribution by using Lindley’sapproximation, based on progressively type-II censoring samples. Noninformativeprior distributions are used for the parameters. Quadratic, linexand general Entropy loss functions are used. The statistical performances of theBayes estimates relative to quadratic, linex and general entropy loss functionsare compared to those of the maximum likelihood based on simulation study.
Three-parameter discontinuous distributions for hydrological samples with zero values
Weglarczyk, Stanislaw; Strupczewski, Witold G.; Singh, Vijay P.
2005-10-01
A consistent approach to the frequency analysis of hydrologic data in arid and semiarid regions, i.e. the data series containing several zero values (e.g. monthly precipitation in dry seasons, annual peak flow discharges, etc.), requires using discontinuous probability distribution functions. Such an approach has received relatively limited attention. Along the lines of physically based models, the extensions of the Muskingum-based models to three parameter forms are considered. Using 44 peak flow series from the USGS data bank, the fitting ability of four three-parameter models was investigated: (1) the Dirac delta combined with Gamma distribution; (2) the Dirac delta combined with two-parameter generalized Pareto distribution; (3) the Dirac delta combined with two-parameter Weibull (DWe) distribution; (4) the kinematic diffusion with one additional parameter that controls the probability of the zero event (KD3). The goodness of fit of the models was assessed and compared both by evaluation of discrepancies between the results of both estimation methods (i.e. the method of moments (MOM) and the maximum likelihood method (MLM)) and using the log of likelihood function as a criterion. In most cases, the DWe distribution with MLM-estimated parameters showed the best fit of all the three-parameter models.
From conservation laws to port-Hamiltonian representations of distributed-parameter systems
Maschke, B.M.; Schaft, van der A.J.; Piztek, P.
2005-01-01
Abstract: In this paper it is shown how the port-Hamiltonian formulation of distributed-parameter systems is closely related to the general thermodynamic framework of systems of conservation laws and closure equations. The situation turns out to be similar to the lumped-parameter case where the Dira
Simulation MLE of Parameters of the Mixture Distribution in the Presence of Two Outliers
Directory of Open Access Journals (Sweden)
Einolah Deiri
2014-12-01
Full Text Available In the presence paper, we deal with the estimation of parameters of the Exponentiated Gamma (EG distribution with presence of multiple(r=2 outliers. The maximum likelihood and moment of the estimators are derived. These estimators are compared empirically using Monte Carlo simulation when all the parameters are unknown. There bias and MSE are investigated with help of numerical technique.
Spatial variability of the parameters of a semi-distributed hydrological model
de Lavenne, Alban; Thirel, Guillaume; Andréassian, Vazken; Perrin, Charles; Ramos, Maria-Helena
2016-05-01
Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).
Mei, Jie; Ren, Wei; Li, Bing; Ma, Guangfu
2015-09-01
In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.
Parlitz, Ulrich; Luther, Stefan
2015-01-01
Features of the Jacobian matrix of the delay coordinates map are exploited for quantifying the robustness and reliability of state and parameter estimations for a given dynamical model using an observed time series. Relevant concepts of this approach are introduced and illustrated for discrete and continuous time systems employing a filtered H\\'enon map and a R\\"ossler system.
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik
1995-01-01
Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...
The Distribution Population-based Genetic Algorithm for Parameter Optimization PID Controller
Institute of Scientific and Technical Information of China (English)
CHENQing-Geng; WANGNing; HUANGShao-Feng
2005-01-01
Enlightened by distribution of creatures in natural ecology environment, the distribution population-based genetic algorithm (DPGA) is presented in this paper. The searching capability of the algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and the simulation results show that satisfactory performances are obtained.
Rosen, I G; Luczak, Susan E; Weiss, Jordan
2014-03-15
We develop a blind deconvolution scheme for input-output systems described by distributed parameter systems with boundary input and output. An abstract functional analytic theory based on results for the linear quadratic control of infinite dimensional systems with unbounded input and output operators is presented. The blind deconvolution problem is then reformulated as a series of constrained linear and nonlinear optimization problems involving infinite dimensional dynamical systems. A finite dimensional approximation and convergence theory is developed. The theory is applied to the problem of estimating blood or breath alcohol concentration (respectively, BAC or BrAC) from biosensor-measured transdermal alcohol concentration (TAC) in the field. A distributed parameter model with boundary input and output is proposed for the transdermal transport of ethanol from the blood through the skin to the sensor. The problem of estimating BAC or BrAC from the TAC data is formulated as a blind deconvolution problem. A scheme to identify distinct drinking episodes in TAC data based on a Hodrick Prescott filter is discussed. Numerical results involving actual patient data are presented.
Manfredi, Sabato
2016-06-01
Large-scale dynamic systems are becoming highly pervasive in their occurrence with applications ranging from system biology, environment monitoring, sensor networks, and power systems. They are characterised by high dimensionality, complexity, and uncertainty in the node dynamic/interactions that require more and more computational demanding methods for their analysis and control design, as well as the network size and node system/interaction complexity increase. Therefore, it is a challenging problem to find scalable computational method for distributed control design of large-scale networks. In this paper, we investigate the robust distributed stabilisation problem of large-scale nonlinear multi-agent systems (briefly MASs) composed of non-identical (heterogeneous) linear dynamical systems coupled by uncertain nonlinear time-varying interconnections. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, new conditions are given for the distributed control design of large-scale MASs that can be easily solved by the toolbox of MATLAB. The stabilisability of each node dynamic is a sufficient assumption to design a global stabilising distributed control. The proposed approach improves some of the existing LMI-based results on MAS by both overcoming their computational limits and extending the applicative scenario to large-scale nonlinear heterogeneous MASs. Additionally, the proposed LMI conditions are further reduced in terms of computational requirement in the case of weakly heterogeneous MASs, which is a common scenario in real application where the network nodes and links are affected by parameter uncertainties. One of the main advantages of the proposed approach is to allow to move from a centralised towards a distributed computing architecture so that the expensive computation workload spent to solve LMIs may be shared among processors located at the networked nodes, thus increasing the scalability of the approach than the network
Directory of Open Access Journals (Sweden)
Yacouba Simporé
2016-01-01
Full Text Available We first prove a null controllability result for a nonlinear system derived from a nonlinear population dynamics model. In order to tackle the controllability problem we use an adapted Carleman inequality. Next we consider the nonlinear population dynamics model with a source term called the pollution term. In order to obtain information on the pollution term we use the method of sentinel.
Directory of Open Access Journals (Sweden)
Anupam Pathak
2014-11-01
Full Text Available Abstract: Problem Statement: The two-parameter exponentiated Rayleigh distribution has been widely used especially in the modelling of life time event data. It provides a statistical model which has a wide variety of application in many areas and the main advantage is its ability in the context of life time event among other distributions. The uniformly minimum variance unbiased and maximum likelihood estimation methods are the way to estimate the parameters of the distribution. In this study we explore and compare the performance of the uniformly minimum variance unbiased and maximum likelihood estimators of the reliability function R(t=P(X>t and P=P(X>Y for the two-parameter exponentiated Rayleigh distribution. Approach: A new technique of obtaining these parametric functions is introduced in which major role is played by the powers of the parameter(s and the functional forms of the parametric functions to be estimated are not needed. We explore the performance of these estimators numerically under varying conditions. Through the simulation study a comparison are made on the performance of these estimators with respect to the Biasness, Mean Square Error (MSE, 95% confidence length and corresponding coverage percentage. Conclusion: Based on the results of simulation study the UMVUES of R(t and ‘P’ for the two-parameter exponentiated Rayleigh distribution found to be superior than MLES of R(t and ‘P’.
Ren, Yefei; Wen, Ruizhi; Yao, Xinxin; Ji, Kun
2017-08-01
The consideration of soil nonlinearity is important for the accurate estimation of the site response. To evaluate the soil nonlinearity during the 2008 Ms8.0 Wenchuan Earthquake, 33 strong-motion records obtained from the main shock and 890 records from 157 aftershocks were collected for this study. The horizontal-to-vertical spectral ratio (HVSR) method was used to calculate five parameters: the ratio of predominant frequency (RFp), degree of nonlinearity (DNL), absolute degree of nonlinearity (ADNL), frequency of nonlinearity (fNL), and percentage of nonlinearity (PNL). The purpose of this study was to evaluate the soil nonlinearity level of 33 strong-motion stations and to investigate the characteristics, performance, and effective usage of these five parameters. Their correlations with the peak ground acceleration (PGA), peak ground velocity (PGV), average uppermost 30-m shear-wave velocity ( V S30), and maximum amplitude of HVSR ( A max) were investigated. The results showed that all five parameters correlate well with PGA and PGV. The DNL, ADNL, and PNL also show a good correlation with A max, which means that the degree of soil nonlinearity not only depends on the ground-motion amplitude (e.g., PGA and PGV) but also on the site condition. The fNL correlates with PGA and PGV but shows no correlation with either A max or V S30, implying that the frequency width affected by the soil nonlinearity predominantly depends on the ground-motion amplitude rather than the site condition. At 16 of the 33 stations analyzed in this study, the site response showed evident (i.e., strong and medium) nonlinearity during the main shock of the Wenchuan Earthquake, where the ground-motion level was almost beyond the threshold of PGA > 200 cm/s2 or PGV > 15 cm/s. The site response showed weak and no nonlinearity at the other 14 and 3 stations. These results also confirm that RFp, DNL, ADNL, and PNL are effective in identifying the soil nonlinearity behavior. The identification
Cernuda, Carlos; Lughofer, Edwin; Klein, Helmut; Forster, Clemens; Pawliczek, Marcin; Brandstetter, Markus
2017-01-01
During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.e., samples are taken from the process and analyzed in the laboratory. This typically requires significant lab technicians efforts for only a small fraction of samples to be analyzed, which leads to significant costs for beer breweries and companies. Fourier transform mid-infrared (FT-MIR) spectroscopy was used in combination with nonlinear multivariate calibration techniques to overcome (i) the time consuming off-line analyses in beer production and (ii) already known limitations of standard linear chemometric methods, like partial least squares (PLS), for important quality parameters Speers et al. (J I Brewing. 2003;109(3):229-235), Zhang et al. (J I Brewing. 2012;118(4):361-367) such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation, or foam stability. The calibration models are established with enhanced nonlinear techniques based (i) on a new piece-wise linear version of PLS by employing fuzzy rules for local partitioning the latent variable space and (ii) on extensions of support vector regression variants (-PLSSVR and ν-PLSSVR), for overcoming high computation times in high-dimensional problems and time-intensive and inappropriate settings of the kernel parameters. Furthermore, we introduce a new model selection scheme based on bagged ensembles in order to improve robustness and thus predictive quality of the final models. The approaches are tested on real-world calibration data sets for wort and beer mix beverages, and successfully compared to
Estimating the distribution of radionuclides in agricultural soils - dependence on soil parameters.
Hormann, Volker; Fischer, Helmut W
2013-10-01
In this study it is shown how radionuclide distributions in agricultural soils and their dependence on soil parameters can be quantitatively estimated. The most important sorption and speciation processes have been implemented into a numerical model using the geochemical code PHREEQC that is able to include specific soil and soil solution compositions. Using this model, distribution coefficients (Kd values) for the elements Cs, Ni, U and Se have been calculated for two different soil types. Furthermore, the dependencies of these Kd values on various soil parameters (e.g. pH value or organic matter content) have been evaluated. It is shown that for each element, an individual set of soil parameters is relevant for its solid-liquid distribution. The model may be used for the calculation of input parameters used by reference biosphere models (e.g. used for the risk assessment of nuclear waste repositories). Copyright © 2013 Elsevier Ltd. All rights reserved.
A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
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Jilin Zhang
2017-09-01
Full Text Available In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT. Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP, which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS. This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.
A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.
Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei
2017-09-21
In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.
Frank, T. D.; Kim, S.; Dotov, D. G.
2013-11-01
Canonical-dissipative nonequilibrium energy distributions play an important role in the life sciences. In one of the most fundamental forms, such energy distributions correspond to two-parametric normal distributions truncated to the left. We present an implicit moment method involving the first and second energy moments to estimate the distribution parameters. It is shown that the method is consistent with Cohen's 1949 formula. The implementation of the algorithm is discussed and the range of admissible parameter values is identified. In addition, an application to an earlier study on human oscillatory hand movements is presented. In this earlier study, energy was conceptualized as the energy of a Hamiltonian oscillator model. The canonical-dissipative approach allows for studying the systematic change of the model parameters with oscillation frequency. It is shown that the results obtained with the implicit moment method are consistent with those derived in the earlier study by other means.
Chien, Lung-Chang; Guo, Yuming; Li, Xiao; Yu, Hwa-Lung
2016-11-16
The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial function into account in the DLNM, and compared the influence with and without considering spatial heterogeneity in a case study. This research applied the DLNM to investigate non-linear lag effect up to 7 days in a case study about the spatiotemporal impact of fine particulate matter (PM2.5) on preschool children's acute respiratory infection in 41 districts of northern Taiwan during 2005 to 2007. We applied two spatiotemporal methods to impute missing air pollutant data, and included the Markov random fields to analyze district boundary data in the DLNM. When analyzing the original data without a spatial function, the overall PM2.5 effect accumulated from all lag-specific effects had a slight variation at smaller PM2.5 measurements, but eventually decreased to relative risk significantly analyzing spatiotemporal imputed data without a spatial function, the overall PM2.5 effect did not decrease but increased in monotone as PM2.5 increased over 20 μg/m(3). After adding a spatial function in the DLNM, spatiotemporal imputed data conducted similar results compared with the overall effect from the original data. Moreover, the spatial function showed a clear and uneven pattern in Taipei, revealing that preschool children living in 31 districts of Taipei were vulnerable to acute respiratory infection. Our findings suggest the necessity of including a spatial function in the DLNM to make a spatiotemporal analysis available and to conduct more reliable and explainable research. This study also revealed the analytical impact if spatial heterogeneity is ignored.Journal of Exposure Science and Environmental Epidemiology advance online publication, 16 November 2016; doi:10.1038/jes
Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
Sun, Xiaodian; Medvedovic, Mario
2016-02-01
Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.
Study of Linear and Non-Linear Optical Parameters of Zinc Selenide Thin Film
Desai, H. N.; J. M. Dhimmar
2015-01-01
Thin film of Zinc Selenide (ZnSe) was deposited onto transparent glass substrate by thermal evaporation technique. ZnSe thin film was characterized by UV-Visible spectrophotometer within the wavelength range of 310 nm-1080 nm. The Linear optical parameters (linear optical absorption, extinction coefficient, refractive index and complex dielectric constant) of ZnSe thin film were analyzed from absorption spectra. The optical band gap and Urbach energy were obtained by Tauc’s equati...
Non-Linear Metamodeling Extensions to the Robust Parameter Design of Computer Simulations
2016-09-15
found to be sufficient for simulation models [43, 54, 60]. The Gaussian correlation function , where 2p = , is also utilized due to its widespread...receptive field defined by the spread parameter nσ . This research employs the Gaussian response function ( ) ( ) 2 ( ) exp 2 n n n n h σ... weighted desirability function approach that allows for varying degrees of importance to be applied to the different responses. The weighted
Fadeev, Viktor V.; Maslov, D. V.; Litvinov, P. N.; Burikov, S. A.
2002-05-01
There is a set of discussed questions in the study of primary processes of the photosynthesis. Solution of these problems stimulates development of new methods for determination of the photo synthetic unit photo physical parameters in-vivo. In the report possibilities of non- linear fluorimetry method in this problem are investigated. The first step requires creation of low-parametrical model of the photosynthesizing organisms fluorescence response formation. The corresponding inverse rpobe4lm can be solved for this model.
Neubert, M.; Winkler, J.
2012-12-01
This contribution continues an article series [1,2] about the nonlinear model-based control of the Czochralski crystal growth process. The key idea of the presented approach is to use a sophisticated combination of nonlinear model-based and conventional (linear) PI controllers for tracking of both, crystal radius and growth rate. Using heater power and pulling speed as manipulated variables several controller structures are possible. The present part tries to systematize the properties of the materials to be grown in order to get unambiguous decision criteria for a most profitable choice of the controller structure. For this purpose a material specific constant M called interface mobility and a more process specific constant S called system response number are introduced. While the first one summarizes important material properties like thermal conductivity and latent heat the latter one characterizes the process by evaluating the average axial thermal gradients at the phase boundary and the actual growth rate at which the crystal is grown. Furthermore these characteristic numbers are useful for establishing a scheduling strategy for the PI controller parameters in order to improve the controller performance. Finally, both numbers give a better understanding of the general thermal system dynamics of the Czochralski technique.
Functional uniform priors for nonlinear modeling.
Bornkamp, Björn
2012-09-01
This article considers the topic of finding prior distributions when a major component of the statistical model depends on a nonlinear function. Using results on how to construct uniform distributions in general metric spaces, we propose a prior distribution that is uniform in the space of functional shapes of the underlying nonlinear function and then back-transform to obtain a prior distribution for the original model parameters. The primary application considered in this article is nonlinear regression, but the idea might be of interest beyond this case. For nonlinear regression the so constructed priors have the advantage that they are parametrization invariant and do not violate the likelihood principle, as opposed to uniform distributions on the parameters or the Jeffrey's prior, respectively. The utility of the proposed priors is demonstrated in the context of design and analysis of nonlinear regression modeling in clinical dose-finding trials, through a real data example and simulation.
Influence of Processing Parameters on Granularity Distribution of Superalloy Powders during PREP
Institute of Scientific and Technical Information of China (English)
Huanming CHEN; Benfu HU; Yiwen ZHANG; Huiying LI; Quanmao YU
2003-01-01
In order to investigate the influence of processing parameters on the granularity distribution of superalloy powders during the atomization of plasma rotating electrode processing (PREP), in this paper FGH95 superalloy powders is prepared under different processing conditions by PREP and the influence of PREP processing parameters on the granularity distribution of FGH95 superalloy powders is discussed based on fractal geometry theory. The results show that with the increase of rotating velocity of the self-consuming electrode, the fractal dimension of the granularity distribution increases linearly, which results in the increase of the proportion of smaller powders. The change of interval between plasma gun and the self-consuming electrode has a little effect on the granularity distribution, also the fractal dimension of the granularity distribution changed a little correspondingly.
Lambert, Ronald J W; Mytilinaios, Ioannis; Maitland, Luke; Brown, Angus M
2012-08-01
This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. However, a disadvantage of using the Excel method was the inability to return confidence intervals for the computed parameters or the correlations between them. Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve.
Directory of Open Access Journals (Sweden)
Dimitrios Kourkoutas
2009-04-01
Full Text Available Dimitrios Kourkoutas1,2, Gerasimos Georgopoulos1, Antonios Maragos1, et al1Department of Ophthalmology, Medical School, Athens University, Athens, Greece; 2Department of Ophthalmology, 417 Hellenic Army Shared Fund Hospital, Athens, GreecePurpose: In this paper a new nonlinear multivariable regression method is presented in order to investigate the relationship between the central corneal thickness (CCT and the Heidelberg Retina Tomograph (HRTII optic nerve head (ONH topographic measurements, in patients with established glaucoma.Methods: Forty nine eyes of 49 patients with glaucoma were included in this study. Inclusion criteria were patients with (a HRT II ONH imaging of good quality (SD 30 < μm, (b reliable Humphrey visual field tests (30-2 program, and (c bilateral CCT measurements with ultrasonic contact pachymetry. Patients were classified as glaucomatous based on visual field and/or ONH damage. The relationship between CCT and topographic parameters was analyzed by using the new nonlinear multivariable regression model.Results: In the entire group, CCT was 549.78 ± 33.08 μm (range: 484–636 μm; intraocular pressure (IOP was 16.4 ± 2.67 mmHg (range: 11–23 mmHg; MD was −3.80 ± 4.97 dB (range: 4.04 – [−20.4] dB; refraction was −0.78 ± 2.46 D (range: −6.0 D to +3.0 D. The new nonlinear multivariable regression model we used indicated that CCT was significantly related (R2 = 0.227, p < 0.01 with rim volume nasally and type of diagnosis.Conclusions: By using the new nonlinear multivariable regression model, in patients with established glaucoma, our data showed that there is a statistically significant correlation between CCT and HRTII ONH structural measurements, in glaucoma patients.Keywords: central corneal thickness, glaucoma, optic nerve head, HRT
Existence of positive solutions for nonlinear dynamic systems with a parameter on a measure chain
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Shuang-Hong Ma
2007-05-01
Full Text Available In this paper, we consider the following dynamic system with parameter on a measure chain $mathbb{T}$, $$displaylines{ u^{DeltaDelta}_{i}(t+lambda h_{i}(tf_{i}(u_{1}(sigma(t, u_{2}(sigma(t,dots ,u_{n}(sigma(t=0,quad tin[a,b], cr alpha u_{i}(a-eta u^{Delta}_{i}(a=0,quad gamma u_{i}(sigma(b+delta u^{Delta}_{i}(sigma(b=0, }$$ where $i=1,2,dots ,n$. Using fixed-point index theory, we find sufficient conditions the existence of positive solutions.
Application of nonlinear neural network to analyze the stope structure parameters
Energy Technology Data Exchange (ETDEWEB)
Lai, X.; Cai, M.; Zhang, B. [University of Science and Technoogy of Beijing (China). Civil and Environmental School
2001-06-01
In this paper, the state-of-the-art of neural computing in geotechnical structural analysis and design has been surveyed. Its computing strategies and research trends are given. The principle of the BP neural networks and computing for constitutive modelling have been discussed, then achieved in applying to analyse the underground stope structure parameters in the Xincheng gold mine with the applications of BP network, it is proven that the neurocomputing is a practical tool for solving large-scale rock underground structural engineering problems. 4 refs., 2 figs., 2 tabs.
Roll maneuvering of flexible aircraft with distributed-parameter actuation via modal synthesis
Oz, Hayrani; Khot, Narendra S.
1999-06-01
The focus is on obtaining and identifying optimal distributed- parameter-control equivalent actuation profiles for desired roll maneuvers by a modal synthesis approach. The solution for distributed-parameter-control of an aeroelastic system is developed by synthesis of modal-state-space controllers designed via the globally power optimal Independent Modal- Space Control (IMSC) technique. The desired maneuver (set- point) control performance is achieved by a modal-performance- output synthesis (MPOS) approach. The MPOS approach requires that each independent modal controller be allocated a desired portion of the total desired output performance. In view of this, a modal performance-output allocation optimization problem is also defined, which minimizes a hybrid measure of control power and elastic strain energy of the structure during aeroelastic control. Insight to distributed-parameter- control equivalent actuation solutions are sought by considering the aeroservoelastic interactions among vehicle motion, aerodynamics, structural flexibility and control actuators from the perspective of work-energy, control power, and control loading requirements. The modal synthesis approach is illustrated for a flight vehicle wing design to achieve a 90 deg/sec roll-rate in a Mach 2 flight condition at altitude (20000 ft) via distributed-parameter equivalent actuation. The preliminary results indicate that such a roll-rate maneuver can be accomplished via distributed-parameter actuation with feasible levels of control power, work-energy, and control loadings through eliciting favorable aeroservoelastic interactions.
Off-line tracking of series parameters in distribution systems using AMI data
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Williams, Tess L.; Sun, Yannan; Schneider, Kevin
2016-05-01
Electric distribution systems have historically lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors at the distribution level is enabling observability. This paper presents an off-line parameter value tracking procedure that takes advantage of the increasing number of measurement devices being deployed at the distribution level to estimate changes in series impedance parameter values over time. The tracking of parameter values enables non-diurnal and non-seasonal change to be flagged for investigation. The presented method uses an unbalanced Distribution System State Estimation (DSSE) and a measurement residual-based parameter estimation procedure. Measurement residuals from multiple measurement snapshots are combined in order to increase the effective local redundancy and improve the robustness of the calculations in the presence of measurement noise. Data from devices on the primary distribution system and from customer meters, via an AMI system, form the input data set. Results of simulations on the IEEE 13-Node Test Feeder are presented to illustrate the proposed approach applied to changes in series impedance parameters. A 5% change in series resistance elements can be detected in the presence of 2% measurement error when combining less than 1 day of measurement snapshots into a single estimate.
Nonlinear Peltier effect in semiconductors
Zebarjadi, Mona; Esfarjani, Keivan; Shakouri, Ali
2007-09-01
Nonlinear Peltier coefficient of a doped InGaAs semiconductor is calculated numerically using the Monte Carlo technique. The Peltier coefficient is also obtained analytically for single parabolic band semiconductors assuming a shifted Fermi-Dirac electronic distribution under an applied bias. Analytical results are in agreement with numerical simulations. Key material parameters affecting the nonlinear behavior are doping concentration, effective mass, and electron-phonon coupling. Current density thresholds at which nonlinear behavior is observable are extracted from numerical data. It is shown that the nonlinear Peltier effect can be used to enhance cooling of thin film microrefrigerator devices especially at low temperatures.
Pasari, S.; Kundu, D.; Dikshit, O.
2012-12-01
Earthquake recurrence interval is one of the important ingredients towards probabilistic seismic hazard assessment (PSHA) for any location. Exponential, gamma, Weibull and lognormal distributions are quite established probability models in this recurrence interval estimation. However, they have certain shortcomings too. Thus, it is imperative to search for some alternative sophisticated distributions. In this paper, we introduce a three-parameter (location, scale and shape) exponentiated exponential distribution and investigate the scope of this distribution as an alternative of the afore-mentioned distributions in earthquake recurrence studies. This distribution is a particular member of the exponentiated Weibull distribution. Despite of its complicated form, it is widely accepted in medical and biological applications. Furthermore, it shares many physical properties with gamma and Weibull family. Unlike gamma distribution, the hazard function of generalized exponential distribution can be easily computed even if the shape parameter is not an integer. To contemplate the plausibility of this model, a complete and homogeneous earthquake catalogue of 20 events (M ≥ 7.0) spanning for the period 1846 to 1995 from North-East Himalayan region (20-32 deg N and 87-100 deg E) has been used. The model parameters are estimated using maximum likelihood estimator (MLE) and method of moment estimator (MOME). No geological or geophysical evidences have been considered in this calculation. The estimated conditional probability reaches quite high after about a decade for an elapsed time of 17 years (i.e. 2012). Moreover, this study shows that the generalized exponential distribution fits the above data events more closely compared to the conventional models and hence it is tentatively concluded that generalized exponential distribution can be effectively considered in earthquake recurrence studies.
A maximum entropy distribution for wave heights of non-linear sea waves
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Based on the maximum entropy principle, a probability density function (PDF) for the zero-crossing wave height (H)of random waves is derived as the simple form fn (H) = αHγe-βHn ( n is a selectable positive integer) through solving a variational problem subject to some quite general constraints. This PDF maximizes the information entropy of H, and its parameters α, γ and β are expressed ear sea waves with large uncertainty, and its parameters can be simply determined from available data. Comparisons between the PDF with n = 3 and n = 4 and the observed distributions of H from wave records measured in the East China Sea and in a wind-wave tunnel show fairly satisfying agreements.
Institute of Scientific and Technical Information of China (English)
Yang Chen; Yong Liu; Xiao-Li Zhou; Li-Xun Zhang; Zhi-Yong Dai; Yong-Zhi Liu
2008-01-01
A novel nonlinear mirror structure which can increase the optical signal-to-noise ratio of a distributed fiber Raman temperature sensor is proposed, and 6 dB improvement of the optical signal-to-noise ratio is obtained. With the assistance of the nonlinear mirror, we demonstrate that the spatial resolution of the sensor is improved from 3 m to 1 m, and the temperature accuracy is improved from ±0.6℃ to ±0.2℃. The theoretical analysis and the experimental data are in good agreement.
Distributed and decentralized state estimation in gas networks as distributed parameter systems.
Ahmadian Behrooz, Hesam; Boozarjomehry, R Bozorgmehry
2015-09-01
In this paper, a framework for distributed and decentralized state estimation in high-pressure and long-distance gas transmission networks (GTNs) is proposed. The non-isothermal model of the plant including mass, momentum and energy balance equations are used to simulate the dynamic behavior. Due to several disadvantages of implementing a centralized Kalman filter for large-scale systems, the continuous/discrete form of extended Kalman filter for distributed and decentralized estimation (DDE) has been extended for these systems. Accordingly, the global model is decomposed into several subsystems, called local models. Some heuristic rules are suggested for system decomposition in gas pipeline networks. In the construction of local models, due to the existence of common states and interconnections among the subsystems, the assimilation and prediction steps of the Kalman filter are modified to take the overlapping and external states into account. However, dynamic Riccati equation for each subsystem is constructed based on the local model, which introduces a maximum error of 5% in the estimated standard deviation of the states in the benchmarks studied in this paper. The performance of the proposed methodology has been shown based on the comparison of its accuracy and computational demands against their counterparts in centralized Kalman filter for two viable benchmarks. In a real life network, it is shown that while the accuracy is not significantly decreased, the real-time factor of the state estimation is increased by a factor of 10.
Rau, Markus Michael; Paech, Kerstin; Seitz, Stella
2016-01-01
Photometric redshift uncertainties are a major source of systematic error for ongoing and future photometric surveys. We study different sources of redshift error caused by common suboptimal binning techniques and propose methods to resolve them. The selection of a too large bin width is shown to oversmooth small scale structure of the radial distribution of galaxies. This systematic error can significantly shift cosmological parameter constraints by up to $6 \\, \\sigma$ for the dark energy equation of state parameter $w$. Careful selection of bin width can reduce this systematic by a factor of up to 6 as compared with commonly used current binning approaches. We further discuss a generalised resampling method that can correct systematic and statistical errors in cosmological parameter constraints caused by uncertainties in the redshift distribution. This can be achieved without any prior assumptions about the shape of the distribution or the form of the redshift error. Our methodology allows photometric surve...
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Patarawan Sangnawakij
2017-02-01
Full Text Available The problem of estimating parameters in a gamma distribution has been widely studied with respect to both theories and applications. In special cases, when the parameter space is bounded, the construction of the confidence interval based on the classical Neyman procedure is unsatisfactory because the information regarding the restriction of the parameter is disregarded. In order to develop the estimator for this issue, the confidence intervals for the coefficient of variation for the case of a gamma distribution were proposed. Extending to two populations, the confidence intervals for the difference and the ratio of coefficients of variation with restricted parameters were presented. Monte Carlo simulations were used to investigate the performance of the proposed estimators. The results showed that the proposed confidence intervals performed better than the compared estimators in terms of expected length, especially when the coefficients of variation were close to the boundary. Additionally, two examples using real data were analyzed to illustrate the findings of the paper.
Parameter estimation for the Pearson type 3 distribution using order statistics
Rocky Durrans, S.
1992-05-01
The Pearson type 3 distribution and its relatives, the log Pearson type 3 and gamma family of distributions, are among the most widely applied in the field of hydrology. Parameter estimation for these distributions has been accomplished using the method of moments, the methods of mixed moments and generalized moments, and the methods of maximum likelihood and maximum entropy. This study evaluates yet another estimation approach, which is based on the use of the properties of an extreme-order statistic. Based on the hypothesis that the population is distributed as Pearson type 3, this estimation approach yields both parameter and 100-year quantile estimators that have lower biases and variances than those of the method of moments approach as recommended by the US Water Resources Council.
Zalvidea; Colautti; Sicre
2000-05-01
An analysis of the Strehl ratio and the optical transfer function as imaging quality parameters of optical elements with enhanced focal length is carried out by employing the Wigner distribution function. To this end, we use four different pupil functions: a full circular aperture, a hyper-Gaussian aperture, a quartic phase plate, and a logarithmic phase mask. A comparison is performed between the quality parameters and test images formed by these pupil functions at different defocus distances.
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Azam Zaka
2014-10-01
Full Text Available This paper is concerned with the modifications of maximum likelihood, moments and percentile estimators of the two parameter Power function distribution. Sampling behavior of the estimators is indicated by Monte Carlo simulation. For some combinations of parameter values, some of the modified estimators appear better than the traditional maximum likelihood, moments and percentile estimators with respect to bias, mean square error and total deviation.
Bayes Estimation of Shape Parameter of Minimax Distribution under Different Loss Functions
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Lanping Li
2015-04-01
Full Text Available The object of this study is to study the Bayes estimation of the unknown shape parameter of Minimax distribution. The prior distribution used here is the non-informative quasi-prior of the parameter. Bayes estimators are derived under squared error loss function and three asymmetric loss functions, which are the LINEX loss, precaution loss and entropy loss functions. Monte Carlo simulations are performed to compare the performances of these Bayes estimates under different situations. Finally, we summarize the result and give the conclusion of this study.
Directory of Open Access Journals (Sweden)
Shujing Su
2015-01-01
Full Text Available For the characteristics of parameters dispersion in large factories, storehouses, and other applications, a distributed parameter measurement system is designed that is based on the ring network. The structure of the system and the circuit design of the master-slave node are described briefly. The basic protocol architecture about transmission communication is introduced, and then this paper comes up with two kinds of distributed transmission control methods. Finally, the reliability, extendibility, and control characteristic of these two methods are tested through a series of experiments. Moreover, the measurement results are compared and discussed.
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Xisheng Dai
2014-01-01
Full Text Available Iterative learning control is an intelligent control algorithm which imitates human learning process. Based on this concept, this paper discussed iterative learning control problem for a class parabolic linear distributed parameter systems with uncertainty coefficients. Iterative learning control algorithm with forgetting factor is proposed and the conditions for convergence of algorithm are established. Combining the matrix theory with the basic theory of distributed parameter systems gives rigorous convergence proof of the algorithm. Finally, by using the forward difference scheme of partial differential equation to solve the problems, the simulation results are presented to illustrate the feasibility of the algorithm.
A method of determining nonlinear large strain consolidation parameters of dredged clays
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Yu-peng CAO
2014-04-01
Full Text Available A method of obtaining the large strain consolidation parameters of dredged clays considering the influence of the initial water content is investigated in this study. According to the test results of remolded clays with high initial water contents reported by Hong et al. (2010, a relationship between the void ratio (e and effective stress (σ' is established. Furthermore, based on the available permeability data from the literature, a new relationship between the permeability coefficient (k and the ratio (e/eL of the void ratio to the void ratio at the liquid limit (eL is proposed. The new proposed expression considering the initial water content improves the e-k equation established by Nagaraj et al. (1994. Finally, the influence of the initial void ratio and effective stress on the large strain consolidation coefficient g(e defined by Gibson et al. (1981 and k/(1+e in large strain analysis is discussed. The results show that, under a constant effective stress, the value of k/(1+e increases with the initial void ratio. The large strain consolidation coefficient shows the law of segmentation change, which decreases with the increase of the effective stress when the effective stress is less than the remolded yield stress, but increases rapidly with the effective stress when the effective stress is larger than the remolded yield stress.
On the estimation of the structure parameter of a normal distribution of order p
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Angelo M. Mineo
2007-10-01
Full Text Available In this paper we compare four different approaches to estimate the structure parameter of a normal distribution of order p (often called exponential power distribution. In particular, we have considered the maximization of the log-likelihood, of the profile log-likelihood, of the conditional profile log-likelihood and a method based on an index of kurtosis. The results of a simulation study seem to indicate the latter approach as the best.
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Meleiro L.A.C.
2000-01-01
Full Text Available Most advanced computer-aided control applications rely on good dynamics process models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-line identification experiments on the process. These experiments for identification often result in input-output data with small output signal-to-noise ratio, and using these data results in inaccurate model parameter estimates [1]. In this work, a multivariable adaptive self-tuning controller (STC was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN. A second approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge about the process being modeled with artificial neural networks. In this case, kinetic parameters - which are very hard to be accurately determined in real time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varying and nonlinear models. This advanced control strategy was applied to a fermentation process to produce ethyl alcohol (ethanol in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the proposed procedure in this work has a great potential for application.
Nagi Reddy, M. V. V.; Pisipati, V. G. K. M.; Madhavi Latha, D.; Datta Prasad, P. V.
2011-02-01
The non-linearity parameter B/ A is estimated for a number of liquid crystal materials of the type N-(p-n-alkoxy benzylidene)-p-n-alkyl anilines, popularly known as nO. m, where n and m are the aliphatic chains on either side of the rigid core, which can be varied from 1 to 18 to realize a number of LC materials with a variety LC phase variants. The B/ A values are computed from both density and sound velocity data following standard relations reported in literature. This systematic study in a homologous series provides an opportunity to study how this parameter behaves with (1) either the alkoxy and/or alkyl chain number, (2) with the total chain number ( n+ m), (3) with increase in molecular weight and (4) whether the linear relations reported in literature either with αT [thermal expansion coefficient ( α) and temperature ( T)] and sound velocity ( u) will hold good or not and if so to what extent. The results are discussed with the body of data available in literature on liquids, liquid mixtures and other LC materials.
Tang, M; Chang, C Q; Fung, P C W; Chau, K T; Chan, F H Y
2005-01-01
The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (λmax) and correlation dimension (D2) alone are somewhat limited in recognition rate. In this paper, improved methods for computing λmaxand D2are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with λmaxand D2so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (Vmax) from recurrence quantification analysis is the best to distinguish different ECG classes. Experimental results using the MIT-BIH Arrhythmia Database show improved and excellent overall accuracy (96.3%), average sensitivity (96.3%) and average specificity (98.15%) for discriminating sinus, premature ventricular contraction and ventricular flutter signals.
Energy Technology Data Exchange (ETDEWEB)
Verma, Dinkar, E-mail: dinkar@iitk.ac.in [Nuclear Engineering and Technology Program, Indian Institute of Technology Kanpur, Kanpur 208 016 (India); Kalra, Manjeet Singh, E-mail: drmanjeet.singh@dituniversity.edu.in [DIT University, Dehradun 248 009 (India); Wahi, Pankaj, E-mail: wahi@iitk.ac.in [Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016 (India)
2017-04-15
Highlights: • A simplified model with nonlinear void reactivity feedback is studied. • Method of multiple scales for nonlinear analysis and oscillation characteristics. • Second order void reactivity dominates in determining system dynamics. • Opposing signs of linear and quadratic void reactivity enhances global safety. - Abstract: In the present work, the effect of nonlinear void reactivity on the dynamics of a simplified lumped-parameter model for a boiling water reactor (BWR) is investigated. A mathematical model of five differential equations comprising of neutronics and thermal-hydraulics encompassing the nonlinearities associated with both the reactivity feedbacks and the heat transfer process has been used. To this end, we have considered parameters relevant to RBMK for which the void reactivity is known to be nonlinear. A nonlinear analysis of the model exploiting the method of multiple time scales (MMTS) predicts the occurrence of the two types of Hopf bifurcation, namely subcritical and supercritical, leading to the evolution of limit cycles for a range of parameters. Numerical simulations have been performed to verify the analytical results obtained by MMTS. The study shows that the nonlinear reactivity has a significant influence on the system dynamics. A parametric study with varying nominal reactor power and operating conditions in coolant channel has also been performed which shows the effect of change in concerned parameter on the boundary between regions of sub- and super-critical Hopf bifurcations in the space constituted by the two coefficients of reactivities viz. the void and the Doppler coefficient of reactivities. In particular, we find that introduction of a negative quadratic term in the void reactivity feedback significantly increases the supercritical region and dominates in determining the system dynamics.
Wan, X.; Tse, P. W.; Xu, G. H.; Tao, T. F.; Zhang, Q.
2016-04-01
Most previous studies on nonlinear Lamb waves are conducted using mode pairs that satisfying strict phase velocity matching and non-zero power flux criteria. However, there are some limitations in existence. First, strict phase velocity matching is not existed in the whole frequency bandwidth; Second, excited center frequency is not always exactly equal to the true phase-velocity-matching frequency; Third, mode pairs are isolated and quite limited in number; Fourth, exciting a single desired primary mode is extremely difficult in practice and the received signal is quite difficult to process and interpret. And few attention has been paid to solving these shortcomings. In this paper, nonlinear S0 mode Lamb waves at low-frequency range satisfying approximate phase velocity matching is proposed for the purpose of overcoming these limitations. In analytical studies, the secondary amplitudes with the propagation distance considering the fundamental frequency, the maximum cumulative propagation distance (MCPD) with the fundamental frequency and the maximum linear cumulative propagation distance (MLCPD) using linear regression analysis are investigated. Based on analytical results, approximate phase velocity matching is quantitatively characterized as the relative phase velocity deviation less than a threshold value of 1%. Numerical studies are also conducted using tone burst as the excitation signal. The influences of center frequency and frequency bandwidth on the secondary amplitudes and MCPD are investigated. S1-S2 mode with the fundamental frequency at 1.8 MHz, the primary S0 mode at the center frequencies of 100 and 200 kHz are used respectively to calculate the ratios of nonlinear parameter of Al 6061-T6 to Al 7075-T651. The close agreement of the computed ratios to the actual value verifies the effectiveness of nonlinear S0 mode Lamb waves satisfying approximate phase velocity matching for characterizing the material nonlinearity. Moreover, the ratios derived from
Exact run length distribution of the double sampling x-bar chart with estimated process parameters
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Teoh, W. L.
2016-05-01
Full Text Available Since the run length distribution is generally highly skewed, a significant concern about focusing too much on the average run length (ARL criterion is that we may miss some crucial information about a control chart’s performance. Thus it is important to investigate the entire run length distribution of a control chart for an in-depth understanding before implementing the chart in process monitoring. In this paper, the percentiles of the run length distribution for the double sampling (DS X chart with estimated process parameters are computed. Knowledge of the percentiles of the run length distribution provides a more comprehensive understanding of the expected behaviour of the run length. This additional information includes the early false alarm, the skewness of the run length distribution, and the median run length (MRL. A comparison of the run length distribution between the optimal ARL-based and MRL-based DS X chart with estimated process parameters is presented in this paper. Examples of applications are given to aid practitioners to select the best design scheme of the DS X chart with estimated process parameters, based on their specific purpose.
Um, Myoung-Jin; Kim, Yeonjoo; Markus, Momcilo; Wuebbles, Donald J.
2017-09-01
Climate extremes, such as heavy precipitation events, have become more common in recent decades, and nonstationarity concepts have increasingly been adopted to model hydrologic extremes. Various issues are associated with applying nonstationary modeling to extremes, and in this study, we focus on assessing the need for different forms of nonlinear functions in a nonstationary generalized extreme value (GEV) model of different annual maximum precipitation (AMP) time series. Moreover, we suggest an efficient approach for selecting the nonlinear functions of a nonstationary GEV model. Based on observed and multiple projected AMP data for eight cities across the U.S., three separate tasks are proposed. First, we conduct trend and stationarity tests for the observed and projected data. Second, AMP series are fit with thirty different nonlinear functions, and the best functions among these are selected. Finally, the selected nonlinear functions are used to model the location parameter of a nonstationary GEV model and stationary and nonstationary GEV models with a linear function. Our results suggest that the simple use of nonlinear functions might prove useful with nonstationary GEV models of AMP for different locations with different types of model results.
Dai, Mi; Wang, Yun
2016-06-01
In order to obtain robust cosmological constraints from Type Ia supernova (SN Ia) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurve fitting. We develop a method for sampling the resultant probability density distributions (pdf) of the SN Ia lightcuve parameters in the MCMC likelihood analysis to constrain cosmological parameters, and validate it using simulated data sets. Applying this method to the `joint lightcurve analysis (JLA)' data set of SNe Ia, we find that sampling the SN Ia lightcurve parameter pdf's leads to cosmological parameters closer to that of a flat Universe with a cosmological constant, compared to the usual practice of using only the best-fitting values of the SN Ia lightcurve parameters. Our method will be useful in the use of SN Ia data for precision cosmology.
Helsel, D.R.; Gilliom, R.J.
1986-01-01
Estimates of distributional parameters (mean, standard deviation, median, interquartile range) are often desired for data sets containing censored observations. Eight methods for estimating these parameters have been evaluated by R. J. Gilliom and D. R. Helsel (this issue) using Monte Carlo simulations. To verify those findings, the same methods are now applied to actual water quality data. The best method (lowest root-mean-squared error (rmse)) over all parameters, sample sizes, and censoring levels is log probability regression (LR), the method found best in the Monte Carlo simulations. Best methods for estimating moment or percentile parameters separately are also identical to the simulations. Reliability of these estimates can be expressed as confidence intervals using rmse and bias values taken from the simulation results. Finally, a new simulation study shows that best methods for estimating uncensored sample statistics from censored data sets are identical to those for estimating population parameters.
Robust nonlinear system identification: Bayesian mixture of experts using the t-distribution
Baldacchino, Tara; Worden, Keith; Rowson, Jennifer
2017-02-01
A novel variational Bayesian mixture of experts model for robust regression of bifurcating and piece-wise continuous processes is introduced. The mixture of experts model is a powerful model which probabilistically splits the input space allowing different models to operate in the separate regions. However, current methods have no fail-safe against outliers. In this paper, a robust mixture of experts model is proposed which consists of Student-t mixture models at the gates and Student-t distributed experts, trained via Bayesian inference. The Student-t distribution has heavier tails than the Gaussian distribution, and so it is more robust to outliers, noise and non-normality in the data. Using both simulated data and real data obtained from the Z24 bridge this robust mixture of experts performs better than its Gaussian counterpart when outliers are present. In particular, it provides robustness to outliers in two forms: unbiased parameter regression models, and robustness to overfitting/complex models.
Barnett, Adrian G; Pan, Xiaochuan; Yu, Weiwei; Tong, Shilu
2011-01-01
Background: Although interest in assessing the impacts of temperature on mortality has increased, few studies have used a case-crossover design to examine nonlinear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature–mortality relationship in China or on what temperature measure is the best predictor of mortality. Objectives: Our objectives were to use a distributed lag nonlinear model (DLNM) as a part of case-crossover design to examine the nonlinear and distributed lag effects of temperature on mortality in Tianjin, China and to explore which temperature measure is the best predictor of mortality. Methods: We applied the DLNM to a case-crossover design to assess the nonlinear and delayed effects of temperatures (maximum, mean, and minimum) on deaths (nonaccidental, cardiopulmonary, cardiovascular, and respiratory). Results: A U-shaped relationship was found consistently between temperature and mortality. Cold effects (i.e., significantly increased mortality associated with low temperatures) were delayed by 3 days and persisted for 10 days. Hot effects (i.e., significantly increased mortality associated with high temperatures) were acute and lasted for 3 days and were followed by mortality displacement for nonaccidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. Conclusions: In Tianjin, extreme cold and hot temperatures increased the risk of mortality. The effects of cold last longer than the effects of heat. Combining the DLNM and the case-crossover design allows the case-crossover design to flexibly estimate the nonlinear and delayed effects of temperature (or air pollution) while controlling for season. PMID:21827978
Athira, P.; Sudheer, K.
2013-12-01
Parameter estimation is one of the major tasks in the application of any physics based distributed model. Generally the calibration does not consider the heterogeneity of the parameters across the basin, and as a result the model simulation conforms to the location for which it has been calibrated for. However, the major advantage of distributed hydrological models is to have reasonably good simulations on various locations in the watershed, including ungauged locations. While multi-site calibration can address this issue to some extent, the availability of more gauge sites in a watershed is always not guaranteed. When single site calibration is performed, generally a uniform variation of the parameters is considered across the basin which does not ensure the true heterogeneity of the parameters in the basin. The primary objective of this study is to compare the effect of uniform variation of the parameter with a procedure that identifies actual heterogeneity of the parameters across the basin, while performing calibration of distributed hydrological models. In order to demonstrate the objective, a case study of two watersheds in the USA using the model, Soil and Water Assessment Tool (SWAT) is presented and discussed. Initially, the SWAT model is calibrated for both the watersheds in the traditional way considering uniform variation of the sensitive parameters during the calibration. Further, the hydrological response units (HRU) delineated in the SWAT are classified into various clusters based the land use, soil type and slope. A random perturbation of the parameters is performed in these clusters during calibration. The rationale behind this approach was to identify plausible parameter values that simulate the hydrological process in these clusters appropriately. The proposed procedure is applied to both the basins. The results indicate that the simulations obtained for upstream ungauged locations (other than that used for calibration) are much better when a
Kuleshova, E. O.; Plyusnin, A. A.; Shandarova, E. B.; Tikhomirova, O. V.
2016-04-01
This paper considers the simulation capability of nonuniform distributed-parameter circuit transients by using MatLab Simulink. This approach is capable of determining currents and voltages of nodes for power networks of any configurations and modes. The paper contains results of nonuniform line simulations in idle, short-circuit and load modes.
On modeling of lifetime data using two-parameter Gamma and Weibull distributions
Shanker, Rama; Shukla, Kamlesh Kumar; Shanker, Ravi; Leonida, Tekie Asehun
2016-01-01
The analysis and modeling of lifetime data are crucial in almost all applied sciences including medicine, insurance, engineering, behavioral sciences and finance, amongst others. The main objective of this paper is to have a comparative study of two-parameter gamma and Weibull distributions for mode
Eliciting hyperparameters of prior distributions for the parameters of paired comparison models
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Nasir Abbas
2013-02-01
Full Text Available Normal 0 false false false EN-US X-NONE AR-SA In the study of paired comparisons (PC, items may be ranked or issues may be prioritized through subjective assessment of certain judges. PC models are developed and then used to serve the purpose of ranking. The PC models may be studied through classical or Bayesian approach. Bayesian inference is a modern statistical technique used to draw conclusions about the population parameters. Its beauty lies in incorporating prior information about the parameters into the analysis in addition to current information (i.e. data. The prior and current information are formally combined to yield a posterior distribution about the population parameters, which is the work bench of the Bayesian statisticians. However, the problems the Bayesians face correspond to the selection and formal utilization of prior distribution. Once the type of prior distribution is decided to be used, the problem of estimating the parameters of the prior distribution (i.e. elicitation still persists. Different methods are devised to serve the purpose. In this study an attempt is made to use Minimum Chi-square (hence forth MCS for the elicitation purpose. Though it is a classical estimation technique, but is used here for the election purpose. The entire elicitation procedure is illustrated through a numerical data set.
Effects of abdominal fat distribution parameters on severity of acute pancreatitis.
LENUS (Irish Health Repository)
O'Leary, D P
2012-07-01
Obesity is a well-established risk factor for acute pancreatitis. Increased visceral fat has been shown to exacerbate the pro-inflammatory milieu experienced by patients. This study aimed to investigate the relationship between the severity of acute pancreatitis and abdominal fat distribution parameters measured on computed tomography (CT) scan.
An EOQ Model with Two-Parameter Weibull Distribution Deterioration and Price-Dependent Demand
Mukhopadhyay, Sushanta; Mukherjee, R. N.; Chaudhuri, K. S.
2005-01-01
An inventory replenishment policy is developed for a deteriorating item and price-dependent demand. The rate of deterioration is taken to be time-proportional and the time to deterioration is assumed to follow a two-parameter Weibull distribution. A power law form of the price dependence of demand is considered. The model is solved analytically…
Empirical Bayes Test for the Parameter of Rayleigh Distribution with Error of Measurement
Institute of Scientific and Technical Information of China (English)
HUANG JUAN
2011-01-01
For the data with error of measurement in historical samples,the empirical Bayes test rule for the parameter of Rayleigh distribution is constructed,and the asymptotically optimal property is obtained.It is shown that the convergence rate of the proposed EB test rule can be arbitrarily close to O(n-1/2) under suitable conditions.
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N. K. Sajeevkumar
2014-09-01
Full Text Available In this article, we derived the Best Linear Unbiased Estimator (BLUE of the location parameter of certain distributions with known coefficient of variation by record values. Efficiency comparisons are also made on the proposed estimator with some of the usual estimators. Finally we give a real life data to explain the utility of results developed in this article.
Izsak, F.
2006-01-01
A numerical maximum likelihood (ML) estimation procedure is developed for the constrained parameters of multinomial distributions. The main dif��?culty involved in computing the likelihood function is the precise and fast determination of the multinomial coef��?cients. For this the coef��?cients are
A New Approach for Parameter Estimation of Mixed Weibull Distribution:A Case Study in Spindle
Institute of Scientific and Technical Information of China (English)
Dongwei Gu; Zhiqiong Wang; Guixiang Shen; Yingzhi Zhang; Xilu Zhao
2016-01-01
In order to improve the accuracy and efficiency of graphical method and maximum likelihood estimation ( MLE) in Mixed Weibull distribution parameters estimation, Graphical-GA combines the advantage of graphical method and genetic algorithm ( GA) is proposed. Firstly, with the analysis of Weibull probability paper (WPP), mixed Weibull is identified to data fitting. Secondly, the observed value of shape and scale parameters are obtained by graphical method with least square, then optimizing the parameters of mixed Weibull with GA. Thirdly, with the comparative analysis on graphical method, piecewise Weibull and two⁃Weibull, it shows graphical⁃GA mixed Weibull is the best. Finally, the spindle MTBF point estimation and interval estimation are got based on mixed Weibull distribution. The results indicate that graphical⁃GA are improved effectively and the evaluation of spindle can provide the basis for design and reliability growth.
Comparison of Two Methods Used to Model Shape Parameters of Pareto Distributions
Liu, C.; Charpentier, R.R.; Su, J.
2011-01-01
Two methods are compared for estimating the shape parameters of Pareto field-size (or pool-size) distributions for petroleum resource assessment. Both methods assume mature exploration in which most of the larger fields have been discovered. Both methods use the sizes of larger discovered fields to estimate the numbers and sizes of smaller fields: (1) the tail-truncated method uses a plot of field size versus size rank, and (2) the log-geometric method uses data binned in field-size classes and the ratios of adjacent bin counts. Simulation experiments were conducted using discovered oil and gas pool-size distributions from four petroleum systems in Alberta, Canada and using Pareto distributions generated by Monte Carlo simulation. The estimates of the shape parameters of the Pareto distributions, calculated by both the tail-truncated and log-geometric methods, generally stabilize where discovered pool numbers are greater than 100. However, with fewer than 100 discoveries, these estimates can vary greatly with each new discovery. The estimated shape parameters of the tail-truncated method are more stable and larger than those of the log-geometric method where the number of discovered pools is more than 100. Both methods, however, tend to underestimate the shape parameter. Monte Carlo simulation was also used to create sequences of discovered pool sizes by sampling from a Pareto distribution with a discovery process model using a defined exploration efficiency (in order to show how biased the sampling was in favor of larger fields being discovered first). A higher (more biased) exploration efficiency gives better estimates of the Pareto shape parameters. ?? 2011 International Association for Mathematical Geosciences.
A distributed probabilistic system for adaptive regulation of image processing parameters.
Morino, V; Foresti, G L; Regazzoni, C S
1996-01-01
A distributed optimization framework and its application to the regulation of the behavior of a network of interacting image processing algorithms are presented. The algorithm parameters used to regulate information extraction are explicitly represented as state variables associated with all network nodes. Nodes are also provided with message-passing procedures to represent dependences between parameter settings at adjacent levels. The regulation problem is defined as a joint-probability maximization of a conditional probabilistic measure evaluated over the space of possible configurations of the whole set of state variables (i.e., parameters). The global optimization problem is partitioned and solved in a distributed way, by considering local probabilistic measures for selecting and estimating the parameters related to specific algorithms used within the network. The problem representation allows a spatially varying tuning of parameters, depending on the different informative contents of the subareas of an image. An application of the proposed approach to an image processing problem is described. The processing chain chosen as an example consists of four modules. The first three algorithms correspond to network nodes. The topmost node is devoted to integrating information derived from applying different parameter settings to the algorithms of the chain. The nodes associated with data-transformation processes to be regulated are represented by an optical sensor and two filtering units (for edge-preserving and edge-extracting filterings), and a straight-segment detection module is used as an integration site.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Firstly, using the damage model for rock based on Lemaitre hypothesis about strain equivalence, a new technique for measuring strength of rock micro-cells by adopting the Mohr-Coulomb criterion was developed, and a statistical damage evolution equation was established based on the property that strength of micro-cells is consistent with normal distribution function, through discussing the characteristics of random distributions for strength of micro-cells, then a statistical damage constitutive model that can simulate the full process of rock strain softening under specific confining pressure was set up. Secondly, a new method to determine the model parameters which can be applied to the situations under different confining pressures was proposed, by deeply studying the relations between the model parameters and characteristic parameters of the full stress-strain curve under different confining pressures. Therefore, a unified statistical damage constitutive model for rock softening which can reflect the effect of different confining pressures was set up. This model makes the physical property of model parameters explicit, contains only conventional mechanical parameters, and leads its application more convenient. Finally, the rationality of this model and its parameters-determining method were identified via comparative analyses between theoretical and experimental curves.
Identification of Synchronous Generator Electric Parameters Connected to the Distribution Grid
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Frolov M. Yu.
2017-04-01
Full Text Available According to modern trends, the power grids with distributed generation will have an open system architecture. It means that active consumers, owners of distributed power units, including mobile units, must have free access to the grid, like when using internet, so it is necessary to have plug and play technologies. Thanks to them, the system will be able to identify the unit type and the unit parameters. Therefore, the main aim of research, described in the paper, was to develop and research a new method of electric parameters identification of synchronous generator. The main feature of the proposed method is that parameter identification is performed while the generator to the grid, so it fits in the technological process of operation of the machine and does not influence on the connection time of the machine. For the implementation of the method, it is not necessary to create dangerous operation modes for the machine or to have additional expensive equipment and it can be used for salient pole machines and round rotor machines. The parameter identification accuracy can be achieved by more accurate account of electromechanical transient process, and making of overdetermined system with many more numbers of equations. Parameter identification will be made with each generator connection to the grid. Comparing data obtained from each connection, the middle values can be find by numerical method, and thus, each subsequent identification will accurate the machine parameters.
Constraining cosmological parameters when taking into account the distribution of candles
Qin, Y P; Dong, Y M; Zhang, F W; Li, H Z; Jia, L W; Mao, L S; Lu, R J; Yi, T F; Cui, X H; Zhang, Z B; Qin, Yi-Ping; Zhang, Bin-Bin; Dong, Yun-Ming; Zhang, Fu-Wen; Li, Huai-Zhen; Jia, Lan-Wei; Mao, Li-Sheng; Lu, Rui-Jing; Yi, Ting-Feng; Cui, Xiao-Hong; Zhang, Zhi-Bin
2005-01-01
We study in detail the effect of the distribution of cosmological candles. First,we propose to perform a Monte-Carlo simulation to check if the hypothesis that there is not a distribution of the deduced relative luminosity distance moduli of a sample, when the measurement uncertainty is negligible, is true. If there exists such a distribution, the statistic chi2 cannot be defined since the distribution itself is unclear. Second, we suggest replacing the conventional minimizing chi2 2 method with the least square method to find the best estimated cosmological parameters due to this definition problem. Once the cosmological parameters are determined with the least square method, the bare distribution (the intrinsic distribution which is independent of the measurement uncertainty)can be estimated and then a lower and upper limits of chi2 can be determined. With these two extreme values of chi2,we are able to make the confidence contour plot in the conventional way. In addition to the gold SN Ia sample and the GR...
Mat Jan, Nur Amalina; Shabri, Ani
2017-01-01
TL-moments approach has been used in an analysis to identify the best-fitting distributions to represent the annual series of maximum streamflow data over seven stations in Johor, Malaysia. The TL-moments with different trimming values are used to estimate the parameter of the selected distributions namely: Three-parameter lognormal (LN3) and Pearson Type III (P3) distribution. The main objective of this study is to derive the TL-moments ( t 1,0), t 1 = 1,2,3,4 methods for LN3 and P3 distributions. The performance of TL-moments ( t 1,0), t 1 = 1,2,3,4 was compared with L-moments through Monte Carlo simulation and streamflow data over a station in Johor, Malaysia. The absolute error is used to test the influence of TL-moments methods on estimated probability distribution functions. From the cases in this study, the results show that TL-moments with four trimmed smallest values from the conceptual sample (TL-moments [4, 0]) of LN3 distribution was the most appropriate in most of the stations of the annual maximum streamflow series in Johor, Malaysia.
Institute of Scientific and Technical Information of China (English)
康盛亮
2001-01-01
Using the modified method of multiple scales, the nonlinear stability of a truncated shallow spherical shell of variable thickness with a nondeformable rigid body at the center under compound loads is investigated. When the geometrical parameter k is larger,the uniformly valid asymptotic solutions of this problem are obtained and the remainder terms are estimated.
Chechenin, N. G.; Khomenko, E. V.; Vainchtein, D. I.; De Hosson, J. Th. M.
2008-01-01
In this report, the nonlinearities are analyzed in fcc-to-bcc (fcc/bcc) population ratio, lattice parameters (a(exp)(fcc)/a(ideal)(fcc) and a(exp)(bcc)/a(ideal)(bcc)) and saturation magnetization (I(S)(obs)/I(S)(a)) of the electrodeposited thin Co-Fe-Ni films as a function of average number of elect
Budic, Lara; Didenko, Gregor; Dormann, Carsten F
2016-01-01
In species distribution analyses, environmental predictors and distribution data for large spatial extents are often available in long-lat format, such as degree raster grids. Long-lat projections suffer from unequal cell sizes, as a degree of longitude decreases in length from approximately 110 km at the equator to 0 km at the poles. Here we investigate whether long-lat and equal-area projections yield similar model parameter estimates, or result in a consistent bias. We analyzed the environmental effects on the distribution of 12 ungulate species with a northern distribution, as models for these species should display the strongest effect of projectional distortion. Additionally we choose four species with entirely continental distributions to investigate the effect of incomplete cell coverage at the coast. We expected that including model weights proportional to the actual cell area should compensate for the observed bias in model coefficients, and similarly that using land coverage of a cell should decrease bias in species with coastal distribution. As anticipated, model coefficients were different between long-lat and equal-area projections. Having progressively smaller and a higher number of cells with increasing latitude influenced the importance of parameters in models, increased the sample size for the northernmost parts of species ranges, and reduced the subcell variability of those areas. However, this bias could be largely removed by weighting long-lat cells by the area they cover, and marginally by correcting for land coverage. Overall we found little effect of using long-lat rather than equal-area projections in our analysis. The fitted relationship between environmental parameters and occurrence probability differed only very little between the two projection types. We still recommend using equal-area projections to avoid possible bias. More importantly, our results suggest that the cell area and the proportion of a cell covered by land should be
Zhan, Hanyu; Voelz, David G.
2016-12-01
The polarimetric bidirectional reflectance distribution function (pBRDF) describes the relationships between incident and scattered Stokes parameters, but the familiar surface-only microfacet pBRDF cannot capture diffuse scattering contributions and depolarization phenomena. We propose a modified pBRDF model with a diffuse scattering component developed from the Kubelka-Munk and Le Hors et al. theories, and apply it in the development of a method to jointly estimate refractive index, slope variance, and diffuse scattering parameters from a series of Stokes parameter measurements of a surface. An application of the model and estimation approach to experimental data published by Priest and Meier shows improved correspondence with measurements of normalized Mueller matrix elements. By converting the Stokes/Mueller calculus formulation of the model to a degree of polarization (DOP) description, the estimation results of the parameters from measured DOP values are found to be consistent with a previous DOP model and results.
Boutalis, Yiannis; Theodoridis, Dimitris C; Christodoulou, Manolis A
2009-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation.
Kang, Ling; Zhang, Song
2016-01-01
Heuristic search algorithms, which are characterized by faster convergence rates and can obtain better solutions than the traditional mathematical methods, are extensively used in engineering optimizations. In this paper, a newly developed elitist-mutated particle swarm optimization (EMPSO) technique and an improved gravitational search algorithm (IGSA) are successively applied to parameter estimation problems of Muskingum flood routing models. First, the global optimization performance of the EMPSO and IGSA are validated by nine standard benchmark functions. Then, to further analyse the applicability of the EMPSO and IGSA for various forms of Muskingum models, three typical structures are considered: the basic two-parameter linear Muskingum model (LMM), a three-parameter nonlinear Muskingum model (NLMM) and a four-parameter nonlinear Muskingum model which incorporates the lateral flow (NLMM-L). The problems are formulated as optimization procedures to minimize the sum of the squared deviations (SSQ) or the sum of the absolute deviations (SAD) between the observed and the estimated outflows. Comparative results of the selected numerical cases (Case 1-3) show that the EMPSO and IGSA not only rapidly converge but also obtain the same best optimal parameter vector in every run. The EMPSO and IGSA exhibit superior robustness and provide two efficient alternative approaches that can be confidently employed to estimate the parameters of both linear and nonlinear Muskingum models in engineering applications.
Directory of Open Access Journals (Sweden)
Ling Kang
Full Text Available Heuristic search algorithms, which are characterized by faster convergence rates and can obtain better solutions than the traditional mathematical methods, are extensively used in engineering optimizations. In this paper, a newly developed elitist-mutated particle swarm optimization (EMPSO technique and an improved gravitational search algorithm (IGSA are successively applied to parameter estimation problems of Muskingum flood routing models. First, the global optimization performance of the EMPSO and IGSA are validated by nine standard benchmark functions. Then, to further analyse the applicability of the EMPSO and IGSA for various forms of Muskingum models, three typical structures are considered: the basic two-parameter linear Muskingum model (LMM, a three-parameter nonlinear Muskingum model (NLMM and a four-parameter nonlinear Muskingum model which incorporates the lateral flow (NLMM-L. The problems are formulated as optimization procedures to minimize the sum of the squared deviations (SSQ or the sum of the absolute deviations (SAD between the observed and the estimated outflows. Comparative results of the selected numerical cases (Case 1-3 show that the EMPSO and IGSA not only rapidly converge but also obtain the same best optimal parameter vector in every run. The EMPSO and IGSA exhibit superior robustness and provide two efficient alternative approaches that can be confidently employed to estimate the parameters of both linear and nonlinear Muskingum models in engineering applications.
Rau, Markus Michael; Hoyle, Ben; Paech, Kerstin; Seitz, Stella
2017-04-01
Photometric redshift uncertainties are a major source of systematic error for ongoing and future photometric surveys. We study different sources of redshift error caused by choosing a suboptimal redshift histogram bin width and propose methods to resolve them. The selection of a too large bin width is shown to oversmooth small-scale structure of the radial distribution of galaxies. This systematic error can significantly shift cosmological parameter constraints by up to 6σ for the dark energy equation-of-state parameter w. Careful selection of bin width can reduce this systematic by a factor of up to 6 as compared with commonly used current binning approaches. We further discuss a generalized resampling method that can correct systematic and statistical errors in cosmological parameter constraints caused by uncertainties in the redshift distribution. This can be achieved without any prior assumptions about the shape of the distribution or the form of the redshift error. Our methodology allows photometric surveys to obtain unbiased cosmological parameter constraints using a minimum number of spectroscopic calibration data. For a DES-like galaxy clustering forecast, we obtain unbiased results with respect to errors caused by suboptimal histogram bin width selection, using only 5k representative spectroscopic calibration objects per tomographic redshift bin.
Directory of Open Access Journals (Sweden)
Michala Jakubcová
2015-01-01
Full Text Available The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.
Abdelrahman, Mahmoud A. E.; Sohaly, M. A.
2017-08-01
This work deals with the construction of the exact traveling wave solutions for the nonlinear Schrödinger equation by the new Riccati-Bernoulli Sub-ODE method. Additionally, we apply this method in order to study the random solutions by finding the probability distribution function when the coefficient in our problem is a random variable. The travelling wave solutions of many equations physically or mathematically are expressed by hyperbolic functions, trigonometric functions and rational functions. We discuss our method in the deterministic case and also in a random case, by studying the beta distribution for the random input.
Energy Technology Data Exchange (ETDEWEB)
Gao, Li-Na; Liu, Fu-Hu [Shanxi University, Institute of Theoretical Physics, Shanxi (China); Lacey, Roy A. [Stony Brook University, Departments of Chemistry and Physics, Stony Brook, NY (United States)
2016-05-15
Experimental results of the transverse-momentum distributions of φ mesons and Ω hyperons produced in gold-gold (Au-Au) collisions with different centrality intervals, measured by the STAR Collaboration at different energies (7.7, 11.5, 19.6, 27, and 39 GeV) in the beam energy scan (BES) program at the relativistic heavy-ion collider (RHIC), are approximately described by the single Erlang distribution and the two-component Schwinger mechanism. Moreover, the STAR experimental transverse-momentum distributions of negatively charged particles, produced in Au-Au collisions at RHIC BES energies, are approximately described by the two-component Erlang distribution and the single Tsallis statistics. The excitation functions of free parameters are obtained from the fit to the experimental data. A weak softest point in the string tension in Ω hyperon spectra is observed at 7.7 GeV. (orig.)
Directory of Open Access Journals (Sweden)
W. Castaings
2009-04-01
Full Text Available Variational methods are widely used for the analysis and control of computationally intensive spatially distributed systems. In particular, the adjoint state method enables a very efficient calculation of the derivatives of an objective function (response function to be analysed or cost function to be optimised with respect to model inputs.
In this contribution, it is shown that the potential of variational methods for distributed catchment scale hydrology should be considered. A distributed flash flood model, coupling kinematic wave overland flow and Green Ampt infiltration, is applied to a small catchment of the Thoré basin and used as a relatively simple (synthetic observations but didactic application case.
It is shown that forward and adjoint sensitivity analysis provide a local but extensive insight on the relation between the assigned model parameters and the simulated hydrological response. Spatially distributed parameter sensitivities can be obtained for a very modest calculation effort (~6 times the computing time of a single model run and the singular value decomposition (SVD of the Jacobian matrix provides an interesting perspective for the analysis of the rainfall-runoff relation.
For the estimation of model parameters, adjoint-based derivatives were found exceedingly efficient in driving a bound-constrained quasi-Newton algorithm. The reference parameter set is retrieved independently from the optimization initial condition when the very common dimension reduction strategy (i.e. scalar multipliers is adopted.
Furthermore, the sensitivity analysis results suggest that most of the variability in this high-dimensional parameter space can be captured with a few orthogonal directions. A parametrization based on the SVD leading singular vectors was found very promising but should be combined with another regularization strategy in order to prevent overfitting.
Milella, Pamela; Bisantino, Tiziana; Gentile, Francesco; Iacobellis, Vito; Trisorio Liuzzi, Giuliana
2012-11-01
SummaryThe paper suggests a methodology, based on performance metrics, to select the optimal set of input and parameters to be used for the simulation of river flow discharges with a semi-distributed hydrologic model. The model is applied at daily scale in a semi-arid basin of Southern Italy (Carapelle river, basin area: 506 km2) for which rainfall and discharge series for the period 2006-2009 are available. A classification of inputs and parameters was made in two subsets: the former - spatially distributed - to be selected among different options, the latter - lumped - to be calibrated. Different data sources of (or methodologies to obtain) spatially distributed data have been explored for the first subset. In particular, the FAO Penman-Monteith, Hargreaves and Thornthwaite equations were tested for the evaluation of reference evapotranspiration that, in semi-arid areas, represents a key role in hydrological modeling. The availability of LAI maps from different remote sensing sources was exploited in order to enhance the characterization of the vegetation state and consequently of the spatio-temporal variation in actual evapotranspiration. Different type of pedotransfer functions were used to derive the soil hydraulic parameters of the area. For each configuration of the first subset of data, a manual calibration of the second subset of parameters was carried out. Both the manual calibration of the lumped parameters and the selection of the optimal distributed dataset were based on the calculation and the comparison of different performance metrics measuring the distance between observed and simulated discharge data series. Results not only show the best options for estimating reference evapotranspiration, crop coefficients, LAI values and hydraulic properties of soil, but also provide significant insights regarding the use of different performance metrics including traditional indexes such as RMSE, NSE, index of agreement, with the more recent Benchmark
Castaings, W.; Dartus, D.; Le Dimet, F.-X.; Saulnier, G.-M.
2009-04-01
Variational methods are widely used for the analysis and control of computationally intensive spatially distributed systems. In particular, the adjoint state method enables a very efficient calculation of the derivatives of an objective function (response function to be analysed or cost function to be optimised) with respect to model inputs. In this contribution, it is shown that the potential of variational methods for distributed catchment scale hydrology should be considered. A distributed flash flood model, coupling kinematic wave overland flow and Green Ampt infiltration, is applied to a small catchment of the Thoré basin and used as a relatively simple (synthetic observations) but didactic application case. It is shown that forward and adjoint sensitivity analysis provide a local but extensive insight on the relation between the assigned model parameters and the simulated hydrological response. Spatially distributed parameter sensitivities can be obtained for a very modest calculation effort (~6 times the computing time of a single model run) and the singular value decomposition (SVD) of the Jacobian matrix provides an interesting perspective for the analysis of the rainfall-runoff relation. For the estimation of model parameters, adjoint-based derivatives were found exceedingly efficient in driving a bound-constrained quasi-Newton algorithm. The reference parameter set is retrieved independently from the optimization initial condition when the very common dimension reduction strategy (i.e. scalar multipliers) is adopted. Furthermore, the sensitivity analysis results suggest that most of the variability in this high-dimensional parameter space can be captured with a few orthogonal directions. A parametrization based on the SVD leading singular vectors was found very promising but should be combined with another regularization strategy in order to prevent overfitting.
Directory of Open Access Journals (Sweden)
Streck Nereu Augusto
2003-01-01
Full Text Available Temperature is a major factor that affects metabolic processes in living organisms. Thermal time has been widely used to account for the effects of temperature on crop growth and development. However, the thermal time approach has been criticized because it assumes a linear relationship between the rate of crop growth or development and temperature. The response of the rate of crop growth and development to temperature is nonlinear. The objective of this study was to develop a generalized nonlinear temperature response function for some growth and developmental parameters in kiwifruit (Actinidia deliciosa (A. Chev. C. F. Liang & A. R. Ferguson. The nonlinear function has three coefficients (the cardinal temperatures, which were 0ºC, 25ºC, and 40ºC. Data of temperature response of relative growth rate, relative leaf area growth, net photosynthesis rate, and leaf appearance rate in kiwifruit (female cv. Hayward at two light levels, which are from published research, were used as independent data for evaluating the performance of the nonlinear and the thermal time functions. The results showed that the generalized nonlinear response function is better than the thermal time approach, and the temperature response of several growth and developmental parameters in kiwifruit can be described with the same response function.
Estimation of Model and Parameter Uncertainty For A Distributed Rainfall-runoff Model
Engeland, K.
The distributed rainfall-runoff model Ecomag is applied as a regional model for nine catchments in the NOPEX area in Sweden. Ecomag calculates streamflow on a daily time resolution. The posterior distribution of the model parameters is conditioned on the observed streamflow in all nine catchments, and calculated using Bayesian statistics. The distribution is estimated by Markov chain Monte Carlo (MCMC). The Bayesian method requires a definition of the likelihood of the parameters. Two alter- native formulations are used. The first formulation is a subjectively chosen objective function describing the goodness of fit between the simulated and observed streamflow as it is used in the GLUE framework. The second formulation is to use a more statis- tically correct likelihood function that describes the simulation errors. The simulation error is defined as the difference between log-transformed observed and simulated streamflows. A statistical model for the simulation errors is constructed. Some param- eters are dependent on the catchment, while others depend on climate. The statistical and the hydrological parameters are estimated simultaneously. Confidence intervals, due to the uncertainty of the Ecomag parameters, for the simulated streamflow are compared for the two likelihood functions. Confidence intervals based on the statis- tical model for the simulation errors are also calculated. The results indicate that the parameter uncertainty depends on the formulation of the likelihood function. The sub- jectively chosen likelihood function gives relatively wide confidence intervals whereas the 'statistical' likelihood function gives more narrow confidence intervals. The statis- tical model for the simulation errors indicates that the structural errors of the model are as least as important as the parameter uncertainty.
Metzler, Holger; Müller, Markus; Sierra, Carlos A.
2017-04-01
Carbon fluxes in the ocean-atmosphere-biosphere system are governed by nonlinear processes, which are usually modeled by a system of ordinary differential equations. It is very difficult to analyze such nonlinear models and to predict their future behavior, particularly their internal age structure: How old is the carbon in different pools (ages) and how old is the carbon that leaves the system (transit times)? How is this age structure modified by the addition of fossil fuel emissions? To answer these questions, we developed a new mathematical approach that allows us to compute and visualize the age structure of models of well mixed pools even if they are nonlinear and nonautonomous. We do not only consider mean ages and mean transit times, but entire distributions. Consequently, we can consider important statistics such as the median, quantiles, or the variance. We applied this mathematical approach to a nonlinear global carbon model consisting of three pools (atmosphere, surface ocean, and terrestrial biosphere) and driven by four emission scenarios (RCP3-PD, RCP4.5, RCP6, RCP8.5). Results showed that the addition of fossil fuels modifies the age structure of C in the atmosphere by drastically increasing its proportion of young carbon. We found little differences among predicted mean ages for the four emission scenarios, but changes in the overall distributions were large with effects on median, quantiles and variance. In the short-term, fossil-fuel emissions have an important effect on the amount of carbon that is exchanged among Earth's main C reservoirs. In the long-term, most added C will eventually end up in the deep ocean, but the time required to return to pre-industrial C age distributions is largely dependent on emission scenarios.
Block-Nordsieck summation and partonic distributions in impact parameter space
Energy Technology Data Exchange (ETDEWEB)
Corsetti, A. [Rome Univ. `La Sapienza (Italy). INFN, Dept. of Physics; Grau, A. [Universidada de Granada (Spain). Dep. de Fisica Teorica y del Cosmos; Pancheri, G. [INFN, Laboratori nazionali di Frascati, Rome (Italy); Srivastava, Y.N. [Perugia Univ. (Italy). INFN, Dept. of Physics
1996-02-01
A model for the parton distributions of hadrons in impact parameter space has been constructed using soft gluon summation. This model incorporates the salient features of distributions obtained from the intrinsic transverse momentum behaviour of hadrons. Under the assumption that the intrinsic behaviour is dominated by soft gluon emission stimulated by the scattering process, the b-spectrum becomes softer and softer as the scattering energy increases. In minijet models for the inclusive cross-sections, this will counter the increase from {sigma}{sub j}et.
McGinn, Christopher F.
2016-12-01
The flow of the quenched energy in imbalanced dijet events has been previously studied by transverse vector sum of charged particles with the CMS detector, namely the missing pT measurement. The results have led to new theoretical insights to order to explain the wide angle radiation. The missing pT technique has been improved so that it allows the study of angular distribution of the energy flow with respect to the dijet axis. The measurements are performed using different distance parameters R with the anti-kT clustering algorithm, which provide information about how the angular distribution of the quenched energy depends on the jet width.
Directory of Open Access Journals (Sweden)
H. C. Winsemius
2008-12-01
Full Text Available In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are constrained by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. The approach has been applied to the ungauged Luangwa river basin (150 000 (km^{2} in Zambia. The information contained in these evaporation estimates imposes compliance of the model with the largest outgoing water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. The model results in turn provide a better understanding of the information density of remotely sensed evaporation. Model parameters to which evaporation is sensitive, have been spatially distributed on the basis of dominant land cover characteristics. Consequently, their values were conditioned by means of Monte-Carlo sampling and evaluation on satellite evaporation estimates. The results show that behavioural parameter sets for model units with similar land cover are indeed clustered. The clustering reveals hydrologically meaningful signatures in the parameter response surface: wetland-dominated areas (also called dambos show optimal parameter ranges that reflect vegetation with a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation which is easily moisture stressed. The forested areas and highlands show parameter ranges that indicate a much deeper root zone which is more drought resistent. Clustering was consequently used to formulate fuzzy membership functions that can be used to constrain parameter realizations in further calibration. Unrealistic parameter ranges, found for instance in the high unsaturated soil zone values in the highlands may indicate either overestimation of satellite-based evaporation or model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of
A heuristic nonlinear constructive method for electric power distribution system reconfiguration
McDermott, Thomas E.
1998-12-01
The electric power distribution system usually operates in a radial configuration, with tie switches between circuits to provide alternate feeds. The losses would be minimized if all switches were closed, but this is not done because it complicates the system's protection against overcurrents. Whenever a component fails, some of the switches must be operated to restore power to as many customers as possible. As loads vary with time, switch operations may reduce losses in the system. Both of these are applications for reconfiguration. The problem is combinatorial, which precludes algorithms that guarantee a global optimum. Most existing reconfiguration algorithms fall into two categories. In the first, branch exchange, the system operates in a feasible radial configuration and the algorithm opens and closes candidate switches in pairs. In the second, loop cutting, the system is completely meshed and the algorithm opens candidate switches to reach a feasible radial configuration. Reconfiguration algorithms based on linearized transshipment, neural networks, heuristics, genetic algorithms, and simulated annealing have also been reported, but not widely used. These existing reconfiguration algorithms work with a simplified model of the power system, and they handle voltage and current constraints approximately, if at all. The algorithm described here is a constructive method, using a full nonlinear power system model that accurately handles constraints. The system starts with all switches open and all failed components isolated. An optional network power flow provides a lower bound on the losses. Then the algorithm closes one switch at a time to minimize the increase in a merit figure, which is the real loss divided by the apparent load served. The merit figure increases with each switch closing. This principle, called discrete ascent optimal programming (DAOP), has been applied to other power system problems, including economic dispatch and phase balancing. For
On the design derivatives of eigenvalues and eigenvectors for distributed parameter systems
Reiss, R.
1985-01-01
In this paper, analytic expressions are obtained for the design derivatives of eigenvalues and eigenfunctions of self-adjoint linear distributed parameter systems. Explicit treatment of boundary conditions is avoided by casting the eigenvalue equation into integral form. Results are expressed in terms of the linear operators defining the eigenvalue problem, and are therefore quite general. Sufficiency conditions appropriate to structural optimization of eigenvalues are obtained.
Patten, W. N.; Robertshaw, H. H.; Pierpont, D.; Wynn, R. H.
1989-01-01
A new, near-optimal feedback control technique is introduced that is shown to provide excellent vibration attenuation for those distributed parameter systems that are often encountered in the areas of aeroservoelasticity and large space systems. The technique relies on a novel solution methodology for the classical optimal control problem. Specifically, the quadratic regulator control problem for a flexible vibrating structure is first cast in a weak functional form that admits an approximate solution. The necessary conditions (first-order) are then solved via a time finite-element method. The procedure produces a low dimensional, algebraic parameterization of the optimal control problem that provides a rigorous basis for a discrete controller with a first-order like hold output. Simulation has shown that the algorithm can successfully control a wide variety of plant forms including multi-input/multi-output systems and systems exhibiting significant nonlinearities. In order to firmly establish the efficacy of the algorithm, a laboratory control experiment was implemented to provide planar (bending) vibration attenuation of a highly flexible beam (with a first clamped-free mode of approximately 0.5 Hz).
On The Estimation of Survival Function and Parameter Exponential Life Time Distribution
Directory of Open Access Journals (Sweden)
Hadeel S. Al-Kutubi
2009-01-01
Full Text Available Problem statement: The study and research of survival or reliability or life time belong to the same area of study but they may belong to a different area of application. In survival analysis one can use several life time distribution, exponential distribution with mean life time θ is one of them. To estimate this parameter and survival function we must be used estimation procedures with less MSE and MPE. Approach: The only statistical theory that combined modeling inherent uncertainty and statistical uncertainty is Bayesian statistics. The theorem of Bayes provided a solution to how learn from data. Bayes theorem was depending on prior and posterior distribution and standard Bayes estimator depends on Jeffery prior information. In this study we annexed Jeffery prior information to get the modify Bayes estimator and then compared it with standard Bayes estimator and maximum likelihood estimator to find the best (less MSE and MPE. Results: when we derived Bayesian and Maximum likelihood of the scale parameter and survival functions. Simulation study was used to compare between estimators and Mean Square Error (MSE and Mean Percentage Error (MPE of estimators are computed. Conclusion: The new proposed estimator of modify Bayes estimator in parameter and survival function was the best estimator (less MSE and MPE when we compared it with standard Bayes and maximum likelihood estimator.
Rafal Podlaski; Francis A. Roesch
2013-01-01
Study assessed the usefulness of various methods for choosing the initial values for the numerical procedures for estimating the parameters of mixture distributions and analysed variety of mixture models to approximate empirical diameter at breast height (dbh) distributions. Two-component mixtures of either the Weibull distribution or the gamma distribution were...
Distributed Consensus of Nonlinear Multi-Agent Systems on State-Controlled Switching Topologies
Directory of Open Access Journals (Sweden)
Kairui Chen
2016-01-01
Full Text Available This paper considers the consensus problem of nonlinear multi-agent systems under switching directed topologies. Specifically, the dynamics of each agent incorporates an intrinsic nonlinear term and the interaction topology may not contain a spanning tree at any time. By designing a state-controlled switching law, we show that the multi-agent system with the neighbor-based protocol can achieve consensus if the switching topologies jointly contain a spanning tree. Moreover, an easily manageable algebraic criterion is deduced to unravel the underlying mechanisms in reaching consensus. Finally, a numerical example is exploited to illustrate the effectiveness of the developed theoretical results.
Teergele, Jane; Danai, Kourosh
2015-12-01
A method of sensor location selection is introduced for distributed parameter systems. In this method, the sensitivities of spatial outputs to model parameters are computed by a model and transformed via continuous wavelet transforms into the time-scale domain to characterise the shape attributes of output sensitivities and accentuate their differences. Regions are then sought in the time-scale plane wherein the wavelet coefficient of an output sensitivity surpasses all the others' as indication of the output sensitivity's distinctness. This yields a comprehensive account of identifiability each output provides to the model parameters as the basis of output selection. The proposed output selection strategy is demonstrated for a numerical case of pollutant dispersion by advection and diffusion in a two-dimensional area.
A new algorithm for importance analysis of the inputs with distribution parameter uncertainty
Li, Luyi; Lu, Zhenzhou
2016-10-01
Importance analysis is aimed at finding the contributions by the inputs to the uncertainty in a model output. For structural systems involving inputs with distribution parameter uncertainty, the contributions by the inputs to the output uncertainty are governed by both the variability and parameter uncertainty in their probability distributions. A natural and consistent way to arrive at importance analysis results in such cases would be a three-loop nested Monte Carlo (MC) sampling strategy, in which the parameters are sampled in the outer loop and the inputs are sampled in the inner nested double-loop. However, the computational effort of this procedure is often prohibitive for engineering problem. This paper, therefore, proposes a newly efficient algorithm for importance analysis of the inputs in the presence of parameter uncertainty. By introducing a 'surrogate sampling probability density function (SS-PDF)' and incorporating the single-loop MC theory into the computation, the proposed algorithm can reduce the original three-loop nested MC computation into a single-loop one in terms of model evaluation, which requires substantially less computational effort. Methods for choosing proper SS-PDF are also discussed in the paper. The efficiency and robustness of the proposed algorithm have been demonstrated by results of several examples.
Worms, Julien
2010-01-01
Let $X_1, \\ldots, X_n$ be some i.i.d. observations from a heavy tailed distribution $F$, i.e. such that the common distribution of the excesses over a high threshold $u_n$ can be approximated by a Generalized Pareto Distribution $G_{\\gamma,\\sigma_n}$ with $\\gamma >0$. This work is devoted to the problem of finding confidence regions for the couple $(\\gamma,\\sigma_n)$ : combining the empirical likelihood methodology with estimation equations (close but not identical to the likelihood equations) introduced by J. Zhang (Australian and New Zealand J. Stat n.49(1), 2007), asymptotically valid confidence regions for $(\\gamma,\\sigma_n)$ are obtained and proved to perform better than Wald-type confidence regions (especially those derived from the asymptotic normality of the maximum likelihood estimators). By profiling out the scale parameter, confidence intervals for the tail index are also derived.
Consistency of change point estimators for symmetrical stable distribution with parameters shift
Institute of Scientific and Technical Information of China (English)
SHI XiaoPing; MIAO BaiQi; GE ChunLei
2008-01-01
Assume that the characteristic index α of stable distribution satisfies 1<α<2, and that the distribution is symmetrical about its mean. We consider the change point estimators for stable distribution with a or scale parameter β shift. For the one case that mean is a known constant, if or β changes, then density function will change too. To this end, we suppose the kernel estimation for a change point. For the other case that mean is an unknown constant, we suppose to apply empirical characteristic function to estimate the change-point location. In the two cases, we consider the consistency and strong convergence rate of estimators. Furthermore, we consider the mean shift case. If mean changes, then corresponding characteristic function will change too. To this end, we also apply empirical characteristic function to estimate change point. We obtain the similar convergence rate. Finally, we consider its application on the detection of mean shift in financial market.
Directory of Open Access Journals (Sweden)
H. C. Winsemius
2008-08-01
Full Text Available In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are estimated by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. A key application for this approach is to identify part of the parameter distribution space in ungauged river basins without the need for ground data. The information, contained in the evaporation estimates implicitly imposes compliance of the model with the largest water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. Furthermore, the model results can provide a better understanding of the information density of remotely sensed evaporation.
The approach has been applied to the ungauged Luangwa river basin (150 000 (km^{2} in Zambia. Model units were delineated on the basis of similar land cover. For each model unit, model parameters for which evaporation is sensitive, have been conditioned on the evaporation estimates by means of Monte-Carlo sampling. The results show that behavioural parameter sets for model units with similar land cover, are indeed clustered. The clustering reveals hydrologically meaningful signatures in the parameter response surface: wetland-dominated areas (also called dambos show optimal parameter ranges that reflect a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation and moisture stressed vegetation. The forested areas and evergreen highlands show parameter ranges that indicate a much deeper root zone and drought resistance.
Unrealistic parameter ranges, found for instance in the high optimal field capacity values in the highlands may indicate model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of groundwater should be included in the model structure. Furthermore, a less distinct
Kelava, Augustin; Werner, Christina S.; Schermelleh-Engel, Karin; Moosbrugger, Helfried; Zapf, Dieter; Ma, Yue; Cham, Heining; Aiken, Leona S.; West, Stephen G.
2011-01-01
Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x[superscript 2] [subscript 1], x[subscript 1]x[subscript 4]) to serve as indicators of each nonlinear latent construct. These approaches require the use of…
Distributed parameter estimation in wireless sensor networks using fused local observations
Fanaei, Mohammad; Valenti, Matthew C.; Schmid, Natalia A.; Alkhweldi, Marwan M.
2012-05-01
The goal of this paper is to reliably estimate a vector of unknown deterministic parameters associated with an underlying function at a fusion center of a wireless sensor network based on its noisy samples made at distributed local sensors. A set of noisy samples of a deterministic function characterized by a nite set of unknown param- eters to be estimated is observed by distributed sensors. The parameters to be estimated can be some attributes associated with the underlying function, such as its height, its center, its variances in dierent directions, or even the weights of its specic components over a predened basis set. Each local sensor processes its observation and sends its processed sample to a fusion center through parallel impaired communication channels. Two local processing schemes, namely analog and digital, are considered. In the analog local processing scheme, each sensor transmits an amplied version of its local analog noisy observation to the fusion center, acting like a relay in a wireless network. In the digital local processing scheme, each sensor quantizes its noisy observation before trans- mitting it to the fusion center. A at-fading channel model is considered between the local sensors and fusion center. The fusion center combines all of the received locally-processed observations and estimates the vector of unknown parameters of the underlying function. Two dierent well-known estimation techniques, namely maximum-likelihood (ML), for both analog and digital local processing schemes, and expectation maximization (EM), for digital local processing scheme, are considered at the fusion center. The performance of the proposed distributed parameter estimation system is investigated through simulation of practical scenarios for a sample underlying function.
Reliable estimation of adsorption isotherm parameters using adequate pore size distribution
Energy Technology Data Exchange (ETDEWEB)
Husseinzadeh, Danial; Shahsavand, Akbar [Ferdowsi University of Mashhad, Mashhad (Iran, Islamic Republic of)
2015-05-15
The equilibrium adsorption isotherm has a crucial effect on various characteristics of the solid adsorbent (e.g., pore volume, bulk density, surface area, pore geometry). A historical paradox exists in conventional estimation of adsorption isotherm parameters. Traditionally, the total amount of adsorb material (total adsorption isotherm) has been considered equivalent to the local adsorption isotherm. This assumption is only valid when the corresponding pore size or energy distribution (PSD or ED) of the porous adsorbent can be successfully represented with the Dirac delta function. In practice, the actual PSD (or ED) is far from such assumption, and the traditional method for prediction of local adsorption isotherm parameters leads to serious errors. Up to now, the powerful combination of inverse theory and linear regularization technique has drastically failed when used for extraction of PSD from real adsorption data. For this reason, all previous researches used synthetic data because they were not able to extract proper PSD from the measured total adsorption isotherm with unrealistic parameters of local adsorption isotherm. We propose a novel approach that can successfully provide the correct values of local adsorption isotherm parameters without any a priori and unrealistic assumptions. Two distinct methods are suggested and several illustrative (synthetic and real experimental) examples are presented to clearly demonstrate the effectiveness of the newly proposed methods on computing the correct values of local adsorption isotherm parameters. The so-called Iterative and Optima methods' impressive performances on extraction of correct PSD are validated using several experimental data sets.
Kostunin, D; Hiller, R; Schröder, F G; Lenok, V; Levinson, E
2016-01-01
We investigate features of the lateral distribution function (LDF) of the radio signal emitted by cosmic ray air-showers with primary energies $> 0.1$~EeV and its connection to air-shower parameters such as energy and shower maximum using CoREAS simulations made for the configuration of the Tunka-Rex antenna array. Taking into account all significant contributions to the total radio emission, such as by the geomagnetic effect, the charge excess, and the atmospheric refraction we parameterize the radio LDF. This parameterization is two-dimensional and has several free parameters. The large number of free parameters is not suitable for experiments of sparse arrays operating at low SNR (signal-to-noise ratios). Thus, exploiting symmetries, we decrease the number of free parameters and reduce the LDF to a simple one-dimensional function. The remaining parameters can be fit with a small number of points, i.e. as few as the signal from three antennas above detection threshold. Finally, we present a method for the r...
Carvalho, C Sofia
2016-01-01
We use a kinematic parametrisation of the luminosity distance to measure the angular distribution on the sky of time derivatives of the scale factor, in particular the Hubble parameter H_0, the deceleration parameter q_0 and the jerk parameter j_0. We apply the method introduced in Carvalho & Marques (2015) to complement probing the inhomogeneity of the large-scale structure by means of the inhomogeneity in the cosmic expansion. This parametrisation is independent of the cosmological equation of state, which renderes it adequate to test interpretations of the cosmic acceleration alternative to the cosmological constant. We also measure the anisotropy of the parameters by computing the power spectrum of the corresponding parameters' maps up to ell=3. Finally for an analytical toy model of an inhomogeneous ensemble of homogenous pixels, we derive the backreaction term in j_0 due to the fluctuations of {H_0,q_0} and measure it to be of order 0.01 the corresponding average over the pixels in the absence of ba...
Yin, L; Daughton, W; Albright, B J; Bezzerides, B; DuBois, D F; Kindel, J M; Vu, H X
2006-02-01
The parametric coupling involving backward stimulated scattering of a laser and electron beam acoustic modes (BAM) is described as observed in particle-in-cell (PIC) simulations. The BAM modes evolve from Langmuir waves (LW) as the electron velocity distribution is nonlinearly modified to be non-Maxwellian by backward stimulated Raman scattering (BSRS). With a marginal damping rate, BAM can be easily excited and allow an extended chirping in frequency to occur as later SRS pulses encounter modified distributions. Coincident with the emergence of this non-Maxwellian distribution is a rapid increase in BSRS reflectivities with laser intensities. Both the reflectivity scaling with laser intensity and the observed spectral features from PIC simulations are consistent with recent Trident experiments.
Energy Technology Data Exchange (ETDEWEB)
Huang, Z. [Department of Physics, University of Arizona, Tucson, Arizona 85741 (United States); Suzuki, M. [Department of Physics and Lawrence Berkeley Laboratory, University of California, Berkeley, California 94720 (United States)
1996-01-01
We obtain the general solutions of the nonlinear {sigma} model in 3+1 dimensions as the candidates for the disoriented chiral condensate (DCC). The nonuniformly isospin-oriented solutions are shown to be related to the uniformly oriented ones through the chiral (axial) rotations. We discuss the pion charge distribution arising from these solutions. The distribution {ital dP}/{ital d}{ital f}=1/(2 {radical}{ital f} ) holds for the uniform solutions in general and the nonuniform solutions in the 1+1 boost-invariant case. For the nonuniform solution in 1+1 without boost invariance and in higher dimensions, the distribution does not hold in the integrated form. However, it is applicable to the pions selected from a small segment in the momentum phase space. We suggest that the nonuniform DCC{close_quote}s may correspond to the mini-Centauro events. {copyright} {ital 1996 The American Physical Society.}
Sepehry-Fard, F.; Coulthard, Maurice H.
1995-01-01
The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.
Sepehry-Fard, F.; Coulthard, Maurice H.
1995-01-01
The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.
Elevated aerosols and role of circulation parameters in aerosol vertical distribution
Prijith, S. S.; Aloysius, Marina; Mohan, Mannil; Rao, P. V. N.
2016-01-01
The study examines aerosol loading in different vertical layers of the atmosphere and explores the role of atmospheric circulation parameters in vertical distribution of aerosols and in its seasonal variability. Aerosol vertical distribution over the globe is examined, using long term satellite observations, by considering aerosol loading in different layers of atmosphere upto ∼6 km altitudes from surface and fractional contribution of each of these layers to total columnar aerosol loading. Aerosols are observed residing close to the surface in most of the oceanic environments, except over certain regions which are in the close proximity of continents where upper level winds are conducive for long range aerosol transport. In contrast, considerable vertical spread in aerosol distribution with strong seasonal variability, minimum occurring in winter months and maximum in summer, is observed over the continental regions. Vertical spread in aerosol distribution is observed highest over north eastern and north western parts of Africa during northern hemispheric summer, when the convection activity peaks over these regions due to large solar insolation and associated surface heating. Seasonal variation of aerosol vertical spread over both of these regions is observed in phase with variation in atmospheric convergence and vorticity. During summer months, when the aerosol vertical spread is highest, strong surface level convergence and associated cyclonic vorticity is observed along with an upper level (700-600 hPa) divergence. The surface level convergence and upper level divergence together induce an upward flow of air which carries aerosols from ground to higher altitudes. This mechanism of aerosol vertical transport is further corroborated through the correlation and regression relations of surface convergence/vorticity with aerosol loading above different elevations and hence the study reveals role of circulation parameters in aerosol vertical distribution.
Estimation of parameters in a distributed precipitation-runoff model for Norway
Directory of Open Access Journals (Sweden)
S. Beldring
2003-01-01
Full Text Available A distributed version of the HBV-model using 1 km2 grid cells and daily time step was used to simulate runoff from the entire land surface of Norway for the period 1961-1990. The model was sensitive to changes in small scale properties of the land surface and the climatic input data, through explicit representation of differences between model elements, and by implicit consideration of sub-grid variations in moisture status. A geographically transferable set of model parameters was determined by a multi-criteria calibration strategy, which simultaneously minimised the residuals between model simulated and observed runoff from 141 Norwegian catchments located in areas with different runoff regimes and landscape characteristics. Model discretisation units with identical landscape classification were assigned similar parameter values. Model performance was evaluated by simulating discharge from 43 independent catchments. Finally, a river routing procedure using a kinematic wave approximation to open channel flow was introduced in the model, and discharges from three additional catchments were calculated and compared with observations. The model was used to produce a map of average annual runoff for Norway for the period 1961-1990. Keywords: distributed model, multi-criteria calibration, global parameters, ungauged catchments.
Mohammadi, Hiwa; Bayrak, Yusuf
2016-04-01
The Gumbel's third asymptotic distribution (GIII) of the extreme value method is employed to evaluate the earthquake hazard parameters in the Iranian Plateau. This research quantifies spatial mapping of earthquake hazard parameters like annual and 100-year mode beside their 90 % probability of not being exceeded (NBE) in the Iranian Plateau. Therefore, we used a homogeneous and complete earthquake catalogue during the period 1900-2013 with magnitude M w ≥ 4.0, and the Iranian Plateau is separated into equal area mesh of 1° late × 1° long. The estimated result of annual mode with 90 % probability of NBE is expected to exceed the values of M w 6.0 in the Eastern part of Makran, most parts of Central and East Iran, Kopeh Dagh, Alborz, Azerbaijan, and SE Zagros. The 100-year mode with 90 % probability of NBE is expected to overpass the value of M w 7.0 in the Eastern part of Makran, Central and East Iran, Alborz, Kopeh Dagh, and Azerbaijan. The spatial distribution of 100-year mode with 90 % probability of NBE uncovers the high values of earthquake hazard parameters which are frequently connected with the main tectonic regimes of the studied area. It appears that there is a close communication among the seismicity and the tectonics of the region.
Bayesian Hierarchical Random Intercept Model Based on Three Parameter Gamma Distribution
Wirawati, Ika; Iriawan, Nur; Irhamah
2017-06-01
Hierarchical data structures are common throughout many areas of research. Beforehand, the existence of this type of data was less noticed in the analysis. The appropriate statistical analysis to handle this type of data is the hierarchical linear model (HLM). This article will focus only on random intercept model (RIM), as a subclass of HLM. This model assumes that the intercept of models in the lowest level are varied among those models, and their slopes are fixed. The differences of intercepts were suspected affected by some variables in the upper level. These intercepts, therefore, are regressed against those upper level variables as predictors. The purpose of this paper would demonstrate a proven work of the proposed two level RIM of the modeling on per capita household expenditure in Maluku Utara, which has five characteristics in the first level and three characteristics of districts/cities in the second level. The per capita household expenditure data in the first level were captured by the three parameters Gamma distribution. The model, therefore, would be more complex due to interaction of many parameters for representing the hierarchical structure and distribution pattern of the data. To simplify the estimation processes of parameters, the computational Bayesian method couple with Markov Chain Monte Carlo (MCMC) algorithm and its Gibbs Sampling are employed.
Yu, Zhang; Xiaohui, Song; Jianfang, Li; Fei, Gao
2017-05-01
Cable overheating will lead to the cable insulation level reducing, speed up the cable insulation aging, even easy to cause short circuit faults. Cable overheating risk identification and warning is nessesary for distribution network operators. Cable overheating risk warning method based on impedance parameter estimation is proposed in the paper to improve the safty and reliability operation of distribution network. Firstly, cable impedance estimation model is established by using least square method based on the data from distribiton SCADA system to improve the impedance parameter estimation accuracy. Secondly, calculate the threshold value of cable impedance based on the historical data and the forecast value of cable impedance based on the forecasting data in future from distribiton SCADA system. Thirdly, establish risks warning rules library of cable overheating, calculate the cable impedance forecast value and analysis the change rate of impedance, and then warn the overheating risk of cable line based on the overheating risk warning rules library according to the variation relationship between impedance and line temperature rise. Overheating risk warning method is simulated in the paper. The simulation results shows that the method can identify the imedance and forecast the temperature rise of cable line in distribution network accurately. The result of overheating risk warning can provide decision basis for operation maintenance and repair.
Directory of Open Access Journals (Sweden)
Álvaro Gutiérrez
2011-11-01
Full Text Available Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA, previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
Directory of Open Access Journals (Sweden)
Tudor DRUGAN
2003-08-01
Full Text Available The aim of the paper was to present the usefulness of the binomial distribution in studying of the contingency tables and the problems of approximation to normality of binomial distribution (the limits, advantages, and disadvantages. The classification of the medical keys parameters reported in medical literature and expressing them using the contingency table units based on their mathematical expressions restrict the discussion of the confidence intervals from 34 parameters to 9 mathematical expressions. The problem of obtaining different information starting with the computed confidence interval for a specified method, information like confidence intervals boundaries, percentages of the experimental errors, the standard deviation of the experimental errors and the deviation relative to significance level was solves through implementation in PHP programming language of original algorithms. The cases of expression, which contain two binomial variables, were separately treated. An original method of computing the confidence interval for the case of two-variable expression was proposed and implemented. The graphical representation of the expression of two binomial variables for which the variation domain of one of the variable depend on the other variable was a real problem because the most of the software used interpolation in graphical representation and the surface maps were quadratic instead of triangular. Based on an original algorithm, a module was implements in PHP in order to represent graphically the triangular surface plots. All the implementation described above was uses in computing the confidence intervals and estimating their performance for binomial distributions sample sizes and variable.
Jevtić, Aleksandar; Gutiérrez, Alvaro
2011-01-01
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed bees algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a genetic algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
Li, Chung-Yi; Ying, Cheng-Ling; Lin, Chun-Yu; Chu, Chien-An
2015-12-01
This study evaluated a directly modulated distributed feedback (DFB) laser diode (LD) for cable TV systems with respect to carrier-to-nonlinear distortion of LDs. The second-order distortion-to-carrier ratio is found to be proportional to that of the second-order coefficient-to-first-order coefficient of the DFB laser diode driving current and to the optical modulation index (OMI). Furthermore, the third-order distortion-to-carrier ratio is proportional to that of the third-order coefficient-to-first-order coefficient of the DFB laser diode driving current, and to the OMI2.
Wang, Mei-Yu; Yan, Feng-Li; Gao, Ting
2016-07-01
We present two deterministic quantum entanglement distribution protocols for a four-photon Dicke polarization entangled state resorting to the frequency and spatial degrees of freedom, which are immune to an arbitrary collective-noise channel. Both of the protocols adopt the X homodyne measurement based on the cross-Kerr nonlinearity to complete the task of the single-photon detection with nearly unit probability in principle. After the four receivers share the photons, they add some local unitary operations to obtain a standard four-photon Dicke polarization entangled state.
Optimal Configuration of Fault-Tolerance Parameters for Distributed Server Access
DEFF Research Database (Denmark)
Daidone, Alessandro; Renier, Thibault; Bondavalli, Andrea
2013-01-01
Server replication is a common fault-tolerance strategy to improve transaction dependability for services in communications networks. In distributed architectures, fault-diagnosis and recovery are implemented via the interaction of the server replicas with the clients and other entities such as e...... in replicated server architectures. In order to obtain insight into the system behaviour, a set of relevant environment parameters and controllable fault-tolerance parameters are chosen and the dependability/performance trade-off is evaluated....... such as enhanced name servers. Such architectures provide an increased number of redundancy configuration choices. The influence of a (wide area) network connection can be quite significant and induce trade-offs between dependability and user-perceived performance. This paper develops a quantitative stochastic...
One-Bit Quantization and Distributed Detection with an Unknown Scale Parameter
Directory of Open Access Journals (Sweden)
Fei Gao
2015-08-01
Full Text Available We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs.